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diff --git a/python-rl-games.spec b/python-rl-games.spec new file mode 100644 index 0000000..63a52f3 --- /dev/null +++ b/python-rl-games.spec @@ -0,0 +1,1203 @@ +%global _empty_manifest_terminate_build 0 +Name: python-rl-games +Version: 1.6.0 +Release: 1 +Summary: please add a summary manually as the author left a blank one +License: MIT +URL: https://github.com/Denys88/rl_games +Source0: https://mirrors.aliyun.com/pypi/web/packages/3e/86/1b66cdbcb7ba92d45238eba64d2e14b77380e4ee2a6f24b706cda140abf8/rl-games-1.6.0.tar.gz +BuildArch: noarch + + +%description +# RL Games: High performance RL library + +## Discord Channel Link +* https://discord.gg/hnYRq7DsQh + +## Papers and related links + +* Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning: https://arxiv.org/abs/2108.10470 +* DeXtreme: Transfer of Agile In-Hand Manipulation from Simulation to Reality: https://dextreme.org/ https://arxiv.org/abs/2210.13702 +* Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger: https://s2r2-ig.github.io/ https://arxiv.org/abs/2108.09779 +* Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge? <https://arxiv.org/abs/2011.09533> +* Superfast Adversarial Motion Priors (AMP) implementation: https://twitter.com/xbpeng4/status/1506317490766303235 https://github.com/NVIDIA-Omniverse/IsaacGymEnvs +* OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation: https://cremebrule.github.io/oscar-web/ https://arxiv.org/abs/2110.00704 +* EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine: https://arxiv.org/abs/2206.10558 and https://github.com/sail-sg/envpool +* TimeChamber: A Massively Parallel Large Scale Self-Play Framework: https://github.com/inspirai/TimeChamber + + +## Some results on the different environments + +* [NVIDIA Isaac Gym](docs/ISAAC_GYM.md) + + + + + + + +* [Dextreme](https://dextreme.org/) + + + +* [Starcraft 2 Multi Agents](docs/SMAC.md) +* [BRAX](docs/BRAX.md) +* [Mujoco Envpool](docs/MUJOCO_ENVPOOL.md) +* [Atari Envpool](docs/ATARI_ENVPOOL.md) +* [Random Envs](docs/OTHER.md) + + +Implemented in Pytorch: + +* PPO with the support of asymmetric actor-critic variant +* Support of end-to-end GPU accelerated training pipeline with Isaac Gym and Brax +* Masked actions support +* Multi-agent training, decentralized and centralized critic variants +* Self-play + + Implemented in Tensorflow 1.x (was removed in this version): + +* Rainbow DQN +* A2C +* PPO + +## Quickstart: Colab in the Cloud + +Explore RL Games quick and easily in colab notebooks: + +* [Mujoco training](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/mujoco_envpool_training.ipynb) Mujoco envpool training example. +* [Brax training](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/brax_training.ipynb) Brax training example, with keeping all the observations and actions on GPU. +* [Onnx discrete space export example with Cartpole](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/train_and_export_onnx_example_discrete.ipynb) envpool training example. +* [Onnx continuous space export example with Pendulum](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/train_and_export_onnx_example_continuous.ipynb) envpool training example. + +## Installation + +For maximum training performance a preliminary installation of Pytorch 1.9+ with CUDA 11.1+ is highly recommended: + +```conda install pytorch torchvision cudatoolkit=11.3 -c pytorch -c nvidia``` or: +```pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html``` + +Then: + +```pip install rl-games``` + +To run CPU-based environments either Ray or envpool are required ```pip install envpool``` or ```pip install ray``` +To run Mujoco, Atari games or Box2d based environments training they need to be additionally installed with ```pip install gym[mujoco]```, ```pip install gym[atari]``` or ```pip install gym[box2d]``` respectively. + +To run Atari also ```pip install opencv-python``` is required. In addition installation of envpool for maximum simulation and training perfromance of Mujoco and Atari environments is highly recommended: ```pip install envpool``` + +## Citing + +If you use rl-games in your research please use the following citation: + +```bibtex +@misc{rl-games2021, +title = {rl-games: A High-performance Framework for Reinforcement Learning}, +author = {Makoviichuk, Denys and Makoviychuk, Viktor}, +month = {May}, +year = {2021}, +publisher = {GitHub}, +journal = {GitHub repository}, +howpublished = {\url{https://github.com/Denys88/rl_games}}, +} +``` + + +## Development setup + +```bash +poetry install +# install cuda related dependencies +poetry run pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html +``` + +## Training +**NVIDIA Isaac Gym** + +Download and follow the installation instructions of Isaac Gym: https://developer.nvidia.com/isaac-gym +And IsaacGymEnvs: https://github.com/NVIDIA-Omniverse/IsaacGymEnvs + +*Ant* + +```python train.py task=Ant headless=True``` +```python train.py task=Ant test=True checkpoint=nn/Ant.pth num_envs=100``` + +*Humanoid* + +```python train.py task=Humanoid headless=True``` +```python train.py task=Humanoid test=True checkpoint=nn/Humanoid.pth num_envs=100``` + +*Shadow Hand block orientation task* + +```python train.py task=ShadowHand headless=True``` +```python train.py task=ShadowHand test=True checkpoint=nn/ShadowHand.pth num_envs=100``` + +**Other** + +*Atari Pong* + +```bash +poetry install -E atari +poetry run python runner.py --train --file rl_games/configs/atari/ppo_pong.yaml +poetry run python runner.py --play --file rl_games/configs/atari/ppo_pong.yaml --checkpoint nn/PongNoFrameskip.pth +``` + +*Brax Ant* + +```bash +poetry install -E brax +poetry run pip install --upgrade "jax[cuda]==0.3.13" -f https://storage.googleapis.com/jax-releases/jax_releases.html +poetry run python runner.py --train --file rl_games/configs/brax/ppo_ant.yaml +poetry run python runner.py --play --file rl_games/configs/brax/ppo_ant.yaml --checkpoint runs/Ant_brax/nn/Ant_brax.pth +``` + +## Experiment tracking + +rl_games support experiment tracking with [Weights and Biases](https://wandb.ai). + +```bash +poetry install -E atari +poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --track +WANDB_API_KEY=xxxx poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --track +poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --wandb-project-name rl-games-special-test --track +poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --wandb-project-name rl-games-special-test -wandb-entity openrlbenchmark --track +``` + + +## Multi GPU + +We use `torchrun` to orchestrate any multi-gpu runs. + +```bash +torchrun --standalone --nnodes=1 --nproc_per_node=2 runner.py --train --file rl_games/configs/ppo_cartpole.yaml +``` + +## Config Parameters + +| Field | Example Value | Default | Description | +| ---------------------- | ------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| seed | 8 | None | Seed for pytorch, numpy etc. | +| algo | | | Algorithm block. | +| name | a2c_continuous | None | Algorithm name. Possible values are: sac, a2c_discrete, a2c_continuous | +| model | | | Model block. | +| name | continuous_a2c_logstd | None | Possible values: continuous_a2c ( expects sigma to be (0, +inf), continuous_a2c_logstd ( expects sigma to be (-inf, +inf), a2c_discrete, a2c_multi_discrete | +| network | | | Network description. | +| name | actor_critic | | Possible values: actor_critic or soft_actor_critic. | +| separate | False | | Whether use or not separate network with same same architecture for critic. In almost all cases if you normalize value it is better to have it False | +| space | | | Network space | +| continuous | | | continuous or discrete | +| mu_activation | None | | Activation for mu. In almost all cases None works the best, but we may try tanh. | +| sigma_activation | None | | Activation for sigma. Will be threated as log(sigma) or sigma depending on model. | +| mu_init | | | Initializer for mu. | +| name | default | | | +| sigma_init | | | Initializer for sigma. if you are using logstd model good value is 0. | +| name | const_initializer | | | +| val | 0 | | | +| fixed_sigma | True | | If true then sigma vector doesn't depend on input. | +| cnn | | | Convolution block. | +| type | conv2d | | Type: right now two types supported: conv2d or conv1d | +| activation | elu | | activation between conv layers. | +| initializer | | | Initialier. I took some names from the tensorflow. | +| name | glorot_normal_initializer | | Initializer name | +| gain | 1.4142 | | Additional parameter. | +| convs | | | Convolution layers. Same parameters as we have in torch. | +| filters | 32 | | Number of filters. | +| kernel_size | 8 | | Kernel size. | +| strides | 4 | | Strides | +| padding | 0 | | Padding | +| filters | 64 | | Next convolution layer info. | +| kernel_size | 4 | | | +| strides | 2 | | | +| padding | 0 | | | +| filters | 64 | | | +| kernel_size | 3 | | | +| strides | 1 | | | +| padding | 0 | | +| mlp | | | MLP Block. Convolution is supported too. See other config examples. | +| units | | | Array of sizes of the MLP layers, for example: [512, 256, 128] | +| d2rl | False | | Use d2rl architecture from https://arxiv.org/abs/2010.09163. | +| activation | elu | | Activations between dense layers. | +| initializer | | | Initializer. | +| name | default | | Initializer name. | +| rnn | | | RNN block. | +| name | lstm | | RNN Layer name. lstm and gru are supported. | +| units | 256 | | Number of units. | +| layers | 1 | | Number of layers | +| before_mlp | False | False | Apply rnn before mlp block or not. | +| config | | | RL Config block. | +| reward_shaper | | | Reward Shaper. Can apply simple transformations. | +| min_val | -1 | | You can apply min_val, max_val, scale and shift. | +| scale_value | 0.1 | 1 | | +| normalize_advantage | True | True | Normalize Advantage. | +| gamma | 0.995 | | Reward Discount | +| tau | 0.95 | | Lambda for GAE. Called tau by mistake long time ago because lambda is keyword in python :( | +| learning_rate | 3e-4 | | Learning rate. | +| name | walker | | Name which will be used in tensorboard. | +| save_best_after | 10 | | How many epochs to wait before start saving checkpoint with best score. | +| score_to_win | 300 | | If score is >=value then this value training will stop. | +| grad_norm | 1.5 | | Grad norm. Applied if truncate_grads is True. Good value is in (1.0, 10.0) | +| entropy_coef | 0 | | Entropy coefficient. Good value for continuous space is 0. For discrete is 0.02 | +| truncate_grads | True | | Apply truncate grads or not. It stabilizes training. | +| env_name | BipedalWalker-v3 | | Envinronment name. | +| e_clip | 0.2 | | clip parameter for ppo loss. | +| clip_value | False | | Apply clip to the value loss. If you are using normalize_value you don't need it. | +| num_actors | 16 | | Number of running actors/environments. | +| horizon_length | 4096 | | Horizon length per each actor. Total number of steps will be num_actors*horizon_length * num_agents (if env is not MA num_agents==1). | +| minibatch_size | 8192 | | Minibatch size. Total number number of steps must be divisible by minibatch size. | +| minibatch_size_per_env | 8 | | Minibatch size per env. If specified will overwrite total number number the default minibatch size with minibatch_size_per_env * nume_envs value. | +| mini_epochs | 4 | | Number of miniepochs. Good value is in [1,10] | +| critic_coef | 2 | | Critic coef. by default critic_loss = critic_coef * 1/2 * MSE. | +| lr_schedule | adaptive | None | Scheduler type. Could be None, linear or adaptive. Adaptive is the best for continuous control tasks. Learning rate is changed changed every miniepoch | +| kl_threshold | 0.008 | | KL threshould for adaptive schedule. if KL < kl_threshold/2 lr = lr * 1.5 and opposite. | +| normalize_input | True | | Apply running mean std for input. | +| bounds_loss_coef | 0.0 | | Coefficient to the auxiary loss for continuous space. | +| max_epochs | 10000 | | Maximum number of epochs to run. | +| max_frames | 5000000 | | Maximum number of frames (env steps) to run. | +| normalize_value | True | | Use value running mean std normalization. | +| use_diagnostics | True | | Adds more information into the tensorboard. | +| value_bootstrap | True | | Bootstraping value when episode is finished. Very useful for different locomotion envs. | +| bound_loss_type | regularisation | None | Adds aux loss for continuous case. 'regularisation' is the sum of sqaured actions. 'bound' is the sum of actions higher than 1.1. | +| bounds_loss_coef | 0.0005 | 0 | Regularisation coefficient | +| use_smooth_clamp | False | | Use smooth clamp instead of regular for cliping | +| zero_rnn_on_done | False | True | If False RNN internal state is not reset (set to 0) when an environment is rest. Could improve training in some cases, for example when domain randomization is on | +| player | | | Player configuration block. | +| render | True | False | Render environment | +| deterministic | True | True | Use deterministic policy ( argmax or mu) or stochastic. | +| use_vecenv | True | False | Use vecenv to create environment for player | +| games_num | 200 | | Number of games to run in the player mode. | +| env_config | | | Env configuration block. It goes directly to the environment. This example was take for my atari wrapper. | +| skip | 4 | | Number of frames to skip | +| name | BreakoutNoFrameskip-v4 | | The exact name of an (atari) gym env. An example, depends on the training env this parameters can be different. | + +## Custom network example: +[simple test network](rl_games/envs/test_network.py) +This network takes dictionary observation. +To register it you can add code in your __init__.py + +``` +from rl_games.envs.test_network import TestNetBuilder +from rl_games.algos_torch import model_builder +model_builder.register_network('testnet', TestNetBuilder) +``` +[simple test environment](rl_games/envs/test/rnn_env.py) +[example environment](rl_games/envs/test/example_env.py) + +Additional environment supported properties and functions + +| Field | Default Value | Description | +| -------------------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| use_central_value | False | If true than returned obs is expected to be dict with 'obs' and 'state' | +| value_size | 1 | Shape of the returned rewards. Network wil support multihead value automatically. | +| concat_infos | False | Should default vecenv convert list of dicts to the dicts of lists. Very usefull if you want to use value_boostrapping. in this case you need to always return 'time_outs' : True or False, from the env. | +| get_number_of_agents(self) | 1 | Returns number of agents in the environment | +| has_action_mask(self) | False | Returns True if environment has invalid actions mask. | +| get_action_mask(self) | None | Returns action masks if has_action_mask is true. Good example is [SMAC Env](rl_games/envs/test/smac_env.py) | + + +## Release Notes + +1.6.0 + +* Added ONNX export colab example for discrete and continious action spaces. For continuous case LSTM policy example is provided as well. +* Improved RNNs training in continuous space, added option `zero_rnn_on_done`. +* Added NVIDIA CuLE support: https://github.com/NVlabs/cule +* Added player config everride. Vecenv is used for inference. +* Fixed multi-gpu training with central value. +* Fixed max_frames termination condition, and it's interaction with the linear learning rate: https://github.com/Denys88/rl_games/issues/212 +* Fixed "deterministic" misspelling issue. +* Fixed Mujoco and Brax SAC configs. +* Fixed multiagent envs statistics reporting. Fixed Starcraft2 SMAC environments. + +1.5.2 + +* Added observation normalization to the SAC. +* Returned back adaptive KL legacy mode. + +1.5.1 + +* Fixed build package issue. + +1.5.0 + +* Added wandb support. +* Added poetry support. +* Fixed various bugs. +* Fixed cnn input was not divided by 255 in case of the dictionary obs. +* Added more envpool mujoco and atari training examples. Some of the results: 15 min Mujoco humanoid training, 2 min atari pong. +* Added Brax and Mujoco colab training examples. +* Added 'seed' command line parameter. Will override seed in config in case it's > 0. +* Deprecated `horovod` in favor of `torch.distributed` ([#171](https://github.com/Denys88/rl_games/pull/171)). + +1.4.0 + +* Added discord channel https://discord.gg/hnYRq7DsQh :) +* Added envpool support with a few atari examples. Works 3-4x time faster than ray. +* Added mujoco results. Much better than openai spinning up ppo results. +* Added tcnn(https://github.com/NVlabs/tiny-cuda-nn) support. Reduces 5-10% of training time in the IsaacGym envs. +* Various fixes and improvements. + +1.3.2 + +* Added 'sigma' command line parameter. Will override sigma for continuous space in case if fixed_sigma is True. + +1.3.1 + +* Fixed SAC not working + +1.3.0 + +* Simplified rnn implementation. Works a little bit slower but much more stable. +* Now central value can be non-rnn if policy is rnn. +* Removed load_checkpoint from the yaml file. now --checkpoint works for both train and play. + +1.2.0 + +* Added Swish (SILU) and GELU activations, it can improve Isaac Gym results for some of the envs. +* Removed tensorflow and made initial cleanup of the old/unused code. +* Simplified runner. +* Now networks are created in the algos with load_network method. + +1.1.4 + +* Fixed crash in a play (test) mode in player, when simulation and rl_devices are not the same. +* Fixed variuos multi gpu errors. + +1.1.3 + +* Fixed crash when running single Isaac Gym environment in a play (test) mode. +* Added config parameter ```clip_actions``` for switching off internal action clipping and rescaling + +1.1.0 + +* Added to pypi: ```pip install rl-games``` +* Added reporting env (sim) step fps, without policy inference. Improved naming. +* Renames in yaml config for better readability: steps_num to horizon_length amd lr_threshold to kl_threshold + + + +## Troubleshouting + +* Some of the supported envs are not installed with setup.py, you need to manually install them +* Starting from rl-games 1.1.0 old yaml configs won't be compatible with the new version: + * ```steps_num``` should be changed to ```horizon_length``` amd ```lr_threshold``` to ```kl_threshold``` + +## Known issues + +* Running a single environment with Isaac Gym can cause crash, if it happens switch to at least 2 environments simulated in parallel + + + + +%package -n python3-rl-games +Summary: please add a summary manually as the author left a blank one +Provides: python-rl-games +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-rl-games +# RL Games: High performance RL library + +## Discord Channel Link +* https://discord.gg/hnYRq7DsQh + +## Papers and related links + +* Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning: https://arxiv.org/abs/2108.10470 +* DeXtreme: Transfer of Agile In-Hand Manipulation from Simulation to Reality: https://dextreme.org/ https://arxiv.org/abs/2210.13702 +* Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger: https://s2r2-ig.github.io/ https://arxiv.org/abs/2108.09779 +* Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge? <https://arxiv.org/abs/2011.09533> +* Superfast Adversarial Motion Priors (AMP) implementation: https://twitter.com/xbpeng4/status/1506317490766303235 https://github.com/NVIDIA-Omniverse/IsaacGymEnvs +* OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation: https://cremebrule.github.io/oscar-web/ https://arxiv.org/abs/2110.00704 +* EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine: https://arxiv.org/abs/2206.10558 and https://github.com/sail-sg/envpool +* TimeChamber: A Massively Parallel Large Scale Self-Play Framework: https://github.com/inspirai/TimeChamber + + +## Some results on the different environments + +* [NVIDIA Isaac Gym](docs/ISAAC_GYM.md) + + + + + + + +* [Dextreme](https://dextreme.org/) + + + +* [Starcraft 2 Multi Agents](docs/SMAC.md) +* [BRAX](docs/BRAX.md) +* [Mujoco Envpool](docs/MUJOCO_ENVPOOL.md) +* [Atari Envpool](docs/ATARI_ENVPOOL.md) +* [Random Envs](docs/OTHER.md) + + +Implemented in Pytorch: + +* PPO with the support of asymmetric actor-critic variant +* Support of end-to-end GPU accelerated training pipeline with Isaac Gym and Brax +* Masked actions support +* Multi-agent training, decentralized and centralized critic variants +* Self-play + + Implemented in Tensorflow 1.x (was removed in this version): + +* Rainbow DQN +* A2C +* PPO + +## Quickstart: Colab in the Cloud + +Explore RL Games quick and easily in colab notebooks: + +* [Mujoco training](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/mujoco_envpool_training.ipynb) Mujoco envpool training example. +* [Brax training](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/brax_training.ipynb) Brax training example, with keeping all the observations and actions on GPU. +* [Onnx discrete space export example with Cartpole](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/train_and_export_onnx_example_discrete.ipynb) envpool training example. +* [Onnx continuous space export example with Pendulum](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/train_and_export_onnx_example_continuous.ipynb) envpool training example. + +## Installation + +For maximum training performance a preliminary installation of Pytorch 1.9+ with CUDA 11.1+ is highly recommended: + +```conda install pytorch torchvision cudatoolkit=11.3 -c pytorch -c nvidia``` or: +```pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html``` + +Then: + +```pip install rl-games``` + +To run CPU-based environments either Ray or envpool are required ```pip install envpool``` or ```pip install ray``` +To run Mujoco, Atari games or Box2d based environments training they need to be additionally installed with ```pip install gym[mujoco]```, ```pip install gym[atari]``` or ```pip install gym[box2d]``` respectively. + +To run Atari also ```pip install opencv-python``` is required. In addition installation of envpool for maximum simulation and training perfromance of Mujoco and Atari environments is highly recommended: ```pip install envpool``` + +## Citing + +If you use rl-games in your research please use the following citation: + +```bibtex +@misc{rl-games2021, +title = {rl-games: A High-performance Framework for Reinforcement Learning}, +author = {Makoviichuk, Denys and Makoviychuk, Viktor}, +month = {May}, +year = {2021}, +publisher = {GitHub}, +journal = {GitHub repository}, +howpublished = {\url{https://github.com/Denys88/rl_games}}, +} +``` + + +## Development setup + +```bash +poetry install +# install cuda related dependencies +poetry run pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html +``` + +## Training +**NVIDIA Isaac Gym** + +Download and follow the installation instructions of Isaac Gym: https://developer.nvidia.com/isaac-gym +And IsaacGymEnvs: https://github.com/NVIDIA-Omniverse/IsaacGymEnvs + +*Ant* + +```python train.py task=Ant headless=True``` +```python train.py task=Ant test=True checkpoint=nn/Ant.pth num_envs=100``` + +*Humanoid* + +```python train.py task=Humanoid headless=True``` +```python train.py task=Humanoid test=True checkpoint=nn/Humanoid.pth num_envs=100``` + +*Shadow Hand block orientation task* + +```python train.py task=ShadowHand headless=True``` +```python train.py task=ShadowHand test=True checkpoint=nn/ShadowHand.pth num_envs=100``` + +**Other** + +*Atari Pong* + +```bash +poetry install -E atari +poetry run python runner.py --train --file rl_games/configs/atari/ppo_pong.yaml +poetry run python runner.py --play --file rl_games/configs/atari/ppo_pong.yaml --checkpoint nn/PongNoFrameskip.pth +``` + +*Brax Ant* + +```bash +poetry install -E brax +poetry run pip install --upgrade "jax[cuda]==0.3.13" -f https://storage.googleapis.com/jax-releases/jax_releases.html +poetry run python runner.py --train --file rl_games/configs/brax/ppo_ant.yaml +poetry run python runner.py --play --file rl_games/configs/brax/ppo_ant.yaml --checkpoint runs/Ant_brax/nn/Ant_brax.pth +``` + +## Experiment tracking + +rl_games support experiment tracking with [Weights and Biases](https://wandb.ai). + +```bash +poetry install -E atari +poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --track +WANDB_API_KEY=xxxx poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --track +poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --wandb-project-name rl-games-special-test --track +poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --wandb-project-name rl-games-special-test -wandb-entity openrlbenchmark --track +``` + + +## Multi GPU + +We use `torchrun` to orchestrate any multi-gpu runs. + +```bash +torchrun --standalone --nnodes=1 --nproc_per_node=2 runner.py --train --file rl_games/configs/ppo_cartpole.yaml +``` + +## Config Parameters + +| Field | Example Value | Default | Description | +| ---------------------- | ------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| seed | 8 | None | Seed for pytorch, numpy etc. | +| algo | | | Algorithm block. | +| name | a2c_continuous | None | Algorithm name. Possible values are: sac, a2c_discrete, a2c_continuous | +| model | | | Model block. | +| name | continuous_a2c_logstd | None | Possible values: continuous_a2c ( expects sigma to be (0, +inf), continuous_a2c_logstd ( expects sigma to be (-inf, +inf), a2c_discrete, a2c_multi_discrete | +| network | | | Network description. | +| name | actor_critic | | Possible values: actor_critic or soft_actor_critic. | +| separate | False | | Whether use or not separate network with same same architecture for critic. In almost all cases if you normalize value it is better to have it False | +| space | | | Network space | +| continuous | | | continuous or discrete | +| mu_activation | None | | Activation for mu. In almost all cases None works the best, but we may try tanh. | +| sigma_activation | None | | Activation for sigma. Will be threated as log(sigma) or sigma depending on model. | +| mu_init | | | Initializer for mu. | +| name | default | | | +| sigma_init | | | Initializer for sigma. if you are using logstd model good value is 0. | +| name | const_initializer | | | +| val | 0 | | | +| fixed_sigma | True | | If true then sigma vector doesn't depend on input. | +| cnn | | | Convolution block. | +| type | conv2d | | Type: right now two types supported: conv2d or conv1d | +| activation | elu | | activation between conv layers. | +| initializer | | | Initialier. I took some names from the tensorflow. | +| name | glorot_normal_initializer | | Initializer name | +| gain | 1.4142 | | Additional parameter. | +| convs | | | Convolution layers. Same parameters as we have in torch. | +| filters | 32 | | Number of filters. | +| kernel_size | 8 | | Kernel size. | +| strides | 4 | | Strides | +| padding | 0 | | Padding | +| filters | 64 | | Next convolution layer info. | +| kernel_size | 4 | | | +| strides | 2 | | | +| padding | 0 | | | +| filters | 64 | | | +| kernel_size | 3 | | | +| strides | 1 | | | +| padding | 0 | | +| mlp | | | MLP Block. Convolution is supported too. See other config examples. | +| units | | | Array of sizes of the MLP layers, for example: [512, 256, 128] | +| d2rl | False | | Use d2rl architecture from https://arxiv.org/abs/2010.09163. | +| activation | elu | | Activations between dense layers. | +| initializer | | | Initializer. | +| name | default | | Initializer name. | +| rnn | | | RNN block. | +| name | lstm | | RNN Layer name. lstm and gru are supported. | +| units | 256 | | Number of units. | +| layers | 1 | | Number of layers | +| before_mlp | False | False | Apply rnn before mlp block or not. | +| config | | | RL Config block. | +| reward_shaper | | | Reward Shaper. Can apply simple transformations. | +| min_val | -1 | | You can apply min_val, max_val, scale and shift. | +| scale_value | 0.1 | 1 | | +| normalize_advantage | True | True | Normalize Advantage. | +| gamma | 0.995 | | Reward Discount | +| tau | 0.95 | | Lambda for GAE. Called tau by mistake long time ago because lambda is keyword in python :( | +| learning_rate | 3e-4 | | Learning rate. | +| name | walker | | Name which will be used in tensorboard. | +| save_best_after | 10 | | How many epochs to wait before start saving checkpoint with best score. | +| score_to_win | 300 | | If score is >=value then this value training will stop. | +| grad_norm | 1.5 | | Grad norm. Applied if truncate_grads is True. Good value is in (1.0, 10.0) | +| entropy_coef | 0 | | Entropy coefficient. Good value for continuous space is 0. For discrete is 0.02 | +| truncate_grads | True | | Apply truncate grads or not. It stabilizes training. | +| env_name | BipedalWalker-v3 | | Envinronment name. | +| e_clip | 0.2 | | clip parameter for ppo loss. | +| clip_value | False | | Apply clip to the value loss. If you are using normalize_value you don't need it. | +| num_actors | 16 | | Number of running actors/environments. | +| horizon_length | 4096 | | Horizon length per each actor. Total number of steps will be num_actors*horizon_length * num_agents (if env is not MA num_agents==1). | +| minibatch_size | 8192 | | Minibatch size. Total number number of steps must be divisible by minibatch size. | +| minibatch_size_per_env | 8 | | Minibatch size per env. If specified will overwrite total number number the default minibatch size with minibatch_size_per_env * nume_envs value. | +| mini_epochs | 4 | | Number of miniepochs. Good value is in [1,10] | +| critic_coef | 2 | | Critic coef. by default critic_loss = critic_coef * 1/2 * MSE. | +| lr_schedule | adaptive | None | Scheduler type. Could be None, linear or adaptive. Adaptive is the best for continuous control tasks. Learning rate is changed changed every miniepoch | +| kl_threshold | 0.008 | | KL threshould for adaptive schedule. if KL < kl_threshold/2 lr = lr * 1.5 and opposite. | +| normalize_input | True | | Apply running mean std for input. | +| bounds_loss_coef | 0.0 | | Coefficient to the auxiary loss for continuous space. | +| max_epochs | 10000 | | Maximum number of epochs to run. | +| max_frames | 5000000 | | Maximum number of frames (env steps) to run. | +| normalize_value | True | | Use value running mean std normalization. | +| use_diagnostics | True | | Adds more information into the tensorboard. | +| value_bootstrap | True | | Bootstraping value when episode is finished. Very useful for different locomotion envs. | +| bound_loss_type | regularisation | None | Adds aux loss for continuous case. 'regularisation' is the sum of sqaured actions. 'bound' is the sum of actions higher than 1.1. | +| bounds_loss_coef | 0.0005 | 0 | Regularisation coefficient | +| use_smooth_clamp | False | | Use smooth clamp instead of regular for cliping | +| zero_rnn_on_done | False | True | If False RNN internal state is not reset (set to 0) when an environment is rest. Could improve training in some cases, for example when domain randomization is on | +| player | | | Player configuration block. | +| render | True | False | Render environment | +| deterministic | True | True | Use deterministic policy ( argmax or mu) or stochastic. | +| use_vecenv | True | False | Use vecenv to create environment for player | +| games_num | 200 | | Number of games to run in the player mode. | +| env_config | | | Env configuration block. It goes directly to the environment. This example was take for my atari wrapper. | +| skip | 4 | | Number of frames to skip | +| name | BreakoutNoFrameskip-v4 | | The exact name of an (atari) gym env. An example, depends on the training env this parameters can be different. | + +## Custom network example: +[simple test network](rl_games/envs/test_network.py) +This network takes dictionary observation. +To register it you can add code in your __init__.py + +``` +from rl_games.envs.test_network import TestNetBuilder +from rl_games.algos_torch import model_builder +model_builder.register_network('testnet', TestNetBuilder) +``` +[simple test environment](rl_games/envs/test/rnn_env.py) +[example environment](rl_games/envs/test/example_env.py) + +Additional environment supported properties and functions + +| Field | Default Value | Description | +| -------------------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| use_central_value | False | If true than returned obs is expected to be dict with 'obs' and 'state' | +| value_size | 1 | Shape of the returned rewards. Network wil support multihead value automatically. | +| concat_infos | False | Should default vecenv convert list of dicts to the dicts of lists. Very usefull if you want to use value_boostrapping. in this case you need to always return 'time_outs' : True or False, from the env. | +| get_number_of_agents(self) | 1 | Returns number of agents in the environment | +| has_action_mask(self) | False | Returns True if environment has invalid actions mask. | +| get_action_mask(self) | None | Returns action masks if has_action_mask is true. Good example is [SMAC Env](rl_games/envs/test/smac_env.py) | + + +## Release Notes + +1.6.0 + +* Added ONNX export colab example for discrete and continious action spaces. For continuous case LSTM policy example is provided as well. +* Improved RNNs training in continuous space, added option `zero_rnn_on_done`. +* Added NVIDIA CuLE support: https://github.com/NVlabs/cule +* Added player config everride. Vecenv is used for inference. +* Fixed multi-gpu training with central value. +* Fixed max_frames termination condition, and it's interaction with the linear learning rate: https://github.com/Denys88/rl_games/issues/212 +* Fixed "deterministic" misspelling issue. +* Fixed Mujoco and Brax SAC configs. +* Fixed multiagent envs statistics reporting. Fixed Starcraft2 SMAC environments. + +1.5.2 + +* Added observation normalization to the SAC. +* Returned back adaptive KL legacy mode. + +1.5.1 + +* Fixed build package issue. + +1.5.0 + +* Added wandb support. +* Added poetry support. +* Fixed various bugs. +* Fixed cnn input was not divided by 255 in case of the dictionary obs. +* Added more envpool mujoco and atari training examples. Some of the results: 15 min Mujoco humanoid training, 2 min atari pong. +* Added Brax and Mujoco colab training examples. +* Added 'seed' command line parameter. Will override seed in config in case it's > 0. +* Deprecated `horovod` in favor of `torch.distributed` ([#171](https://github.com/Denys88/rl_games/pull/171)). + +1.4.0 + +* Added discord channel https://discord.gg/hnYRq7DsQh :) +* Added envpool support with a few atari examples. Works 3-4x time faster than ray. +* Added mujoco results. Much better than openai spinning up ppo results. +* Added tcnn(https://github.com/NVlabs/tiny-cuda-nn) support. Reduces 5-10% of training time in the IsaacGym envs. +* Various fixes and improvements. + +1.3.2 + +* Added 'sigma' command line parameter. Will override sigma for continuous space in case if fixed_sigma is True. + +1.3.1 + +* Fixed SAC not working + +1.3.0 + +* Simplified rnn implementation. Works a little bit slower but much more stable. +* Now central value can be non-rnn if policy is rnn. +* Removed load_checkpoint from the yaml file. now --checkpoint works for both train and play. + +1.2.0 + +* Added Swish (SILU) and GELU activations, it can improve Isaac Gym results for some of the envs. +* Removed tensorflow and made initial cleanup of the old/unused code. +* Simplified runner. +* Now networks are created in the algos with load_network method. + +1.1.4 + +* Fixed crash in a play (test) mode in player, when simulation and rl_devices are not the same. +* Fixed variuos multi gpu errors. + +1.1.3 + +* Fixed crash when running single Isaac Gym environment in a play (test) mode. +* Added config parameter ```clip_actions``` for switching off internal action clipping and rescaling + +1.1.0 + +* Added to pypi: ```pip install rl-games``` +* Added reporting env (sim) step fps, without policy inference. Improved naming. +* Renames in yaml config for better readability: steps_num to horizon_length amd lr_threshold to kl_threshold + + + +## Troubleshouting + +* Some of the supported envs are not installed with setup.py, you need to manually install them +* Starting from rl-games 1.1.0 old yaml configs won't be compatible with the new version: + * ```steps_num``` should be changed to ```horizon_length``` amd ```lr_threshold``` to ```kl_threshold``` + +## Known issues + +* Running a single environment with Isaac Gym can cause crash, if it happens switch to at least 2 environments simulated in parallel + + + + +%package help +Summary: Development documents and examples for rl-games +Provides: python3-rl-games-doc +%description help +# RL Games: High performance RL library + +## Discord Channel Link +* https://discord.gg/hnYRq7DsQh + +## Papers and related links + +* Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning: https://arxiv.org/abs/2108.10470 +* DeXtreme: Transfer of Agile In-Hand Manipulation from Simulation to Reality: https://dextreme.org/ https://arxiv.org/abs/2210.13702 +* Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger: https://s2r2-ig.github.io/ https://arxiv.org/abs/2108.09779 +* Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge? <https://arxiv.org/abs/2011.09533> +* Superfast Adversarial Motion Priors (AMP) implementation: https://twitter.com/xbpeng4/status/1506317490766303235 https://github.com/NVIDIA-Omniverse/IsaacGymEnvs +* OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation: https://cremebrule.github.io/oscar-web/ https://arxiv.org/abs/2110.00704 +* EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine: https://arxiv.org/abs/2206.10558 and https://github.com/sail-sg/envpool +* TimeChamber: A Massively Parallel Large Scale Self-Play Framework: https://github.com/inspirai/TimeChamber + + +## Some results on the different environments + +* [NVIDIA Isaac Gym](docs/ISAAC_GYM.md) + + + + + + + +* [Dextreme](https://dextreme.org/) + + + +* [Starcraft 2 Multi Agents](docs/SMAC.md) +* [BRAX](docs/BRAX.md) +* [Mujoco Envpool](docs/MUJOCO_ENVPOOL.md) +* [Atari Envpool](docs/ATARI_ENVPOOL.md) +* [Random Envs](docs/OTHER.md) + + +Implemented in Pytorch: + +* PPO with the support of asymmetric actor-critic variant +* Support of end-to-end GPU accelerated training pipeline with Isaac Gym and Brax +* Masked actions support +* Multi-agent training, decentralized and centralized critic variants +* Self-play + + Implemented in Tensorflow 1.x (was removed in this version): + +* Rainbow DQN +* A2C +* PPO + +## Quickstart: Colab in the Cloud + +Explore RL Games quick and easily in colab notebooks: + +* [Mujoco training](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/mujoco_envpool_training.ipynb) Mujoco envpool training example. +* [Brax training](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/brax_training.ipynb) Brax training example, with keeping all the observations and actions on GPU. +* [Onnx discrete space export example with Cartpole](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/train_and_export_onnx_example_discrete.ipynb) envpool training example. +* [Onnx continuous space export example with Pendulum](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/train_and_export_onnx_example_continuous.ipynb) envpool training example. + +## Installation + +For maximum training performance a preliminary installation of Pytorch 1.9+ with CUDA 11.1+ is highly recommended: + +```conda install pytorch torchvision cudatoolkit=11.3 -c pytorch -c nvidia``` or: +```pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html``` + +Then: + +```pip install rl-games``` + +To run CPU-based environments either Ray or envpool are required ```pip install envpool``` or ```pip install ray``` +To run Mujoco, Atari games or Box2d based environments training they need to be additionally installed with ```pip install gym[mujoco]```, ```pip install gym[atari]``` or ```pip install gym[box2d]``` respectively. + +To run Atari also ```pip install opencv-python``` is required. In addition installation of envpool for maximum simulation and training perfromance of Mujoco and Atari environments is highly recommended: ```pip install envpool``` + +## Citing + +If you use rl-games in your research please use the following citation: + +```bibtex +@misc{rl-games2021, +title = {rl-games: A High-performance Framework for Reinforcement Learning}, +author = {Makoviichuk, Denys and Makoviychuk, Viktor}, +month = {May}, +year = {2021}, +publisher = {GitHub}, +journal = {GitHub repository}, +howpublished = {\url{https://github.com/Denys88/rl_games}}, +} +``` + + +## Development setup + +```bash +poetry install +# install cuda related dependencies +poetry run pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html +``` + +## Training +**NVIDIA Isaac Gym** + +Download and follow the installation instructions of Isaac Gym: https://developer.nvidia.com/isaac-gym +And IsaacGymEnvs: https://github.com/NVIDIA-Omniverse/IsaacGymEnvs + +*Ant* + +```python train.py task=Ant headless=True``` +```python train.py task=Ant test=True checkpoint=nn/Ant.pth num_envs=100``` + +*Humanoid* + +```python train.py task=Humanoid headless=True``` +```python train.py task=Humanoid test=True checkpoint=nn/Humanoid.pth num_envs=100``` + +*Shadow Hand block orientation task* + +```python train.py task=ShadowHand headless=True``` +```python train.py task=ShadowHand test=True checkpoint=nn/ShadowHand.pth num_envs=100``` + +**Other** + +*Atari Pong* + +```bash +poetry install -E atari +poetry run python runner.py --train --file rl_games/configs/atari/ppo_pong.yaml +poetry run python runner.py --play --file rl_games/configs/atari/ppo_pong.yaml --checkpoint nn/PongNoFrameskip.pth +``` + +*Brax Ant* + +```bash +poetry install -E brax +poetry run pip install --upgrade "jax[cuda]==0.3.13" -f https://storage.googleapis.com/jax-releases/jax_releases.html +poetry run python runner.py --train --file rl_games/configs/brax/ppo_ant.yaml +poetry run python runner.py --play --file rl_games/configs/brax/ppo_ant.yaml --checkpoint runs/Ant_brax/nn/Ant_brax.pth +``` + +## Experiment tracking + +rl_games support experiment tracking with [Weights and Biases](https://wandb.ai). + +```bash +poetry install -E atari +poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --track +WANDB_API_KEY=xxxx poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --track +poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --wandb-project-name rl-games-special-test --track +poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_torch.yaml --wandb-project-name rl-games-special-test -wandb-entity openrlbenchmark --track +``` + + +## Multi GPU + +We use `torchrun` to orchestrate any multi-gpu runs. + +```bash +torchrun --standalone --nnodes=1 --nproc_per_node=2 runner.py --train --file rl_games/configs/ppo_cartpole.yaml +``` + +## Config Parameters + +| Field | Example Value | Default | Description | +| ---------------------- | ------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| seed | 8 | None | Seed for pytorch, numpy etc. | +| algo | | | Algorithm block. | +| name | a2c_continuous | None | Algorithm name. Possible values are: sac, a2c_discrete, a2c_continuous | +| model | | | Model block. | +| name | continuous_a2c_logstd | None | Possible values: continuous_a2c ( expects sigma to be (0, +inf), continuous_a2c_logstd ( expects sigma to be (-inf, +inf), a2c_discrete, a2c_multi_discrete | +| network | | | Network description. | +| name | actor_critic | | Possible values: actor_critic or soft_actor_critic. | +| separate | False | | Whether use or not separate network with same same architecture for critic. In almost all cases if you normalize value it is better to have it False | +| space | | | Network space | +| continuous | | | continuous or discrete | +| mu_activation | None | | Activation for mu. In almost all cases None works the best, but we may try tanh. | +| sigma_activation | None | | Activation for sigma. Will be threated as log(sigma) or sigma depending on model. | +| mu_init | | | Initializer for mu. | +| name | default | | | +| sigma_init | | | Initializer for sigma. if you are using logstd model good value is 0. | +| name | const_initializer | | | +| val | 0 | | | +| fixed_sigma | True | | If true then sigma vector doesn't depend on input. | +| cnn | | | Convolution block. | +| type | conv2d | | Type: right now two types supported: conv2d or conv1d | +| activation | elu | | activation between conv layers. | +| initializer | | | Initialier. I took some names from the tensorflow. | +| name | glorot_normal_initializer | | Initializer name | +| gain | 1.4142 | | Additional parameter. | +| convs | | | Convolution layers. Same parameters as we have in torch. | +| filters | 32 | | Number of filters. | +| kernel_size | 8 | | Kernel size. | +| strides | 4 | | Strides | +| padding | 0 | | Padding | +| filters | 64 | | Next convolution layer info. | +| kernel_size | 4 | | | +| strides | 2 | | | +| padding | 0 | | | +| filters | 64 | | | +| kernel_size | 3 | | | +| strides | 1 | | | +| padding | 0 | | +| mlp | | | MLP Block. Convolution is supported too. See other config examples. | +| units | | | Array of sizes of the MLP layers, for example: [512, 256, 128] | +| d2rl | False | | Use d2rl architecture from https://arxiv.org/abs/2010.09163. | +| activation | elu | | Activations between dense layers. | +| initializer | | | Initializer. | +| name | default | | Initializer name. | +| rnn | | | RNN block. | +| name | lstm | | RNN Layer name. lstm and gru are supported. | +| units | 256 | | Number of units. | +| layers | 1 | | Number of layers | +| before_mlp | False | False | Apply rnn before mlp block or not. | +| config | | | RL Config block. | +| reward_shaper | | | Reward Shaper. Can apply simple transformations. | +| min_val | -1 | | You can apply min_val, max_val, scale and shift. | +| scale_value | 0.1 | 1 | | +| normalize_advantage | True | True | Normalize Advantage. | +| gamma | 0.995 | | Reward Discount | +| tau | 0.95 | | Lambda for GAE. Called tau by mistake long time ago because lambda is keyword in python :( | +| learning_rate | 3e-4 | | Learning rate. | +| name | walker | | Name which will be used in tensorboard. | +| save_best_after | 10 | | How many epochs to wait before start saving checkpoint with best score. | +| score_to_win | 300 | | If score is >=value then this value training will stop. | +| grad_norm | 1.5 | | Grad norm. Applied if truncate_grads is True. Good value is in (1.0, 10.0) | +| entropy_coef | 0 | | Entropy coefficient. Good value for continuous space is 0. For discrete is 0.02 | +| truncate_grads | True | | Apply truncate grads or not. It stabilizes training. | +| env_name | BipedalWalker-v3 | | Envinronment name. | +| e_clip | 0.2 | | clip parameter for ppo loss. | +| clip_value | False | | Apply clip to the value loss. If you are using normalize_value you don't need it. | +| num_actors | 16 | | Number of running actors/environments. | +| horizon_length | 4096 | | Horizon length per each actor. Total number of steps will be num_actors*horizon_length * num_agents (if env is not MA num_agents==1). | +| minibatch_size | 8192 | | Minibatch size. Total number number of steps must be divisible by minibatch size. | +| minibatch_size_per_env | 8 | | Minibatch size per env. If specified will overwrite total number number the default minibatch size with minibatch_size_per_env * nume_envs value. | +| mini_epochs | 4 | | Number of miniepochs. Good value is in [1,10] | +| critic_coef | 2 | | Critic coef. by default critic_loss = critic_coef * 1/2 * MSE. | +| lr_schedule | adaptive | None | Scheduler type. Could be None, linear or adaptive. Adaptive is the best for continuous control tasks. Learning rate is changed changed every miniepoch | +| kl_threshold | 0.008 | | KL threshould for adaptive schedule. if KL < kl_threshold/2 lr = lr * 1.5 and opposite. | +| normalize_input | True | | Apply running mean std for input. | +| bounds_loss_coef | 0.0 | | Coefficient to the auxiary loss for continuous space. | +| max_epochs | 10000 | | Maximum number of epochs to run. | +| max_frames | 5000000 | | Maximum number of frames (env steps) to run. | +| normalize_value | True | | Use value running mean std normalization. | +| use_diagnostics | True | | Adds more information into the tensorboard. | +| value_bootstrap | True | | Bootstraping value when episode is finished. Very useful for different locomotion envs. | +| bound_loss_type | regularisation | None | Adds aux loss for continuous case. 'regularisation' is the sum of sqaured actions. 'bound' is the sum of actions higher than 1.1. | +| bounds_loss_coef | 0.0005 | 0 | Regularisation coefficient | +| use_smooth_clamp | False | | Use smooth clamp instead of regular for cliping | +| zero_rnn_on_done | False | True | If False RNN internal state is not reset (set to 0) when an environment is rest. Could improve training in some cases, for example when domain randomization is on | +| player | | | Player configuration block. | +| render | True | False | Render environment | +| deterministic | True | True | Use deterministic policy ( argmax or mu) or stochastic. | +| use_vecenv | True | False | Use vecenv to create environment for player | +| games_num | 200 | | Number of games to run in the player mode. | +| env_config | | | Env configuration block. It goes directly to the environment. This example was take for my atari wrapper. | +| skip | 4 | | Number of frames to skip | +| name | BreakoutNoFrameskip-v4 | | The exact name of an (atari) gym env. An example, depends on the training env this parameters can be different. | + +## Custom network example: +[simple test network](rl_games/envs/test_network.py) +This network takes dictionary observation. +To register it you can add code in your __init__.py + +``` +from rl_games.envs.test_network import TestNetBuilder +from rl_games.algos_torch import model_builder +model_builder.register_network('testnet', TestNetBuilder) +``` +[simple test environment](rl_games/envs/test/rnn_env.py) +[example environment](rl_games/envs/test/example_env.py) + +Additional environment supported properties and functions + +| Field | Default Value | Description | +| -------------------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| use_central_value | False | If true than returned obs is expected to be dict with 'obs' and 'state' | +| value_size | 1 | Shape of the returned rewards. Network wil support multihead value automatically. | +| concat_infos | False | Should default vecenv convert list of dicts to the dicts of lists. Very usefull if you want to use value_boostrapping. in this case you need to always return 'time_outs' : True or False, from the env. | +| get_number_of_agents(self) | 1 | Returns number of agents in the environment | +| has_action_mask(self) | False | Returns True if environment has invalid actions mask. | +| get_action_mask(self) | None | Returns action masks if has_action_mask is true. Good example is [SMAC Env](rl_games/envs/test/smac_env.py) | + + +## Release Notes + +1.6.0 + +* Added ONNX export colab example for discrete and continious action spaces. For continuous case LSTM policy example is provided as well. +* Improved RNNs training in continuous space, added option `zero_rnn_on_done`. +* Added NVIDIA CuLE support: https://github.com/NVlabs/cule +* Added player config everride. Vecenv is used for inference. +* Fixed multi-gpu training with central value. +* Fixed max_frames termination condition, and it's interaction with the linear learning rate: https://github.com/Denys88/rl_games/issues/212 +* Fixed "deterministic" misspelling issue. +* Fixed Mujoco and Brax SAC configs. +* Fixed multiagent envs statistics reporting. Fixed Starcraft2 SMAC environments. + +1.5.2 + +* Added observation normalization to the SAC. +* Returned back adaptive KL legacy mode. + +1.5.1 + +* Fixed build package issue. + +1.5.0 + +* Added wandb support. +* Added poetry support. +* Fixed various bugs. +* Fixed cnn input was not divided by 255 in case of the dictionary obs. +* Added more envpool mujoco and atari training examples. Some of the results: 15 min Mujoco humanoid training, 2 min atari pong. +* Added Brax and Mujoco colab training examples. +* Added 'seed' command line parameter. Will override seed in config in case it's > 0. +* Deprecated `horovod` in favor of `torch.distributed` ([#171](https://github.com/Denys88/rl_games/pull/171)). + +1.4.0 + +* Added discord channel https://discord.gg/hnYRq7DsQh :) +* Added envpool support with a few atari examples. Works 3-4x time faster than ray. +* Added mujoco results. Much better than openai spinning up ppo results. +* Added tcnn(https://github.com/NVlabs/tiny-cuda-nn) support. Reduces 5-10% of training time in the IsaacGym envs. +* Various fixes and improvements. + +1.3.2 + +* Added 'sigma' command line parameter. Will override sigma for continuous space in case if fixed_sigma is True. + +1.3.1 + +* Fixed SAC not working + +1.3.0 + +* Simplified rnn implementation. Works a little bit slower but much more stable. +* Now central value can be non-rnn if policy is rnn. +* Removed load_checkpoint from the yaml file. now --checkpoint works for both train and play. + +1.2.0 + +* Added Swish (SILU) and GELU activations, it can improve Isaac Gym results for some of the envs. +* Removed tensorflow and made initial cleanup of the old/unused code. +* Simplified runner. +* Now networks are created in the algos with load_network method. + +1.1.4 + +* Fixed crash in a play (test) mode in player, when simulation and rl_devices are not the same. +* Fixed variuos multi gpu errors. + +1.1.3 + +* Fixed crash when running single Isaac Gym environment in a play (test) mode. +* Added config parameter ```clip_actions``` for switching off internal action clipping and rescaling + +1.1.0 + +* Added to pypi: ```pip install rl-games``` +* Added reporting env (sim) step fps, without policy inference. Improved naming. +* Renames in yaml config for better readability: steps_num to horizon_length amd lr_threshold to kl_threshold + + + +## Troubleshouting + +* Some of the supported envs are not installed with setup.py, you need to manually install them +* Starting from rl-games 1.1.0 old yaml configs won't be compatible with the new version: + * ```steps_num``` should be changed to ```horizon_length``` amd ```lr_threshold``` to ```kl_threshold``` + +## Known issues + +* Running a single environment with Isaac Gym can cause crash, if it happens switch to at least 2 environments simulated in parallel + + + + +%prep +%autosetup -n rl-games-1.6.0 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-rl-games -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.6.0-1 +- Package Spec generated |