%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? * 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) ![Ant_running](https://user-images.githubusercontent.com/463063/125260924-a5969800-e2b5-11eb-931c-116cc90d4bbe.gif) ![Humanoid_running](https://user-images.githubusercontent.com/463063/125266095-4edf8d00-e2ba-11eb-9c1a-4dc1524adf71.gif) ![Allegro_Hand_400](https://user-images.githubusercontent.com/463063/125261559-38373700-e2b6-11eb-80eb-b250a0693f0b.gif) ![Shadow_Hand_OpenAI](https://user-images.githubusercontent.com/463063/125262637-328e2100-e2b7-11eb-99af-ea546a53f66a.gif) * [Dextreme](https://dextreme.org/) ![Allegro_Hand_real_world](https://user-images.githubusercontent.com/463063/216529475-3adeddea-94c3-4ac0-99db-00e7df4ba54b.gif) * [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? * 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) ![Ant_running](https://user-images.githubusercontent.com/463063/125260924-a5969800-e2b5-11eb-931c-116cc90d4bbe.gif) ![Humanoid_running](https://user-images.githubusercontent.com/463063/125266095-4edf8d00-e2ba-11eb-9c1a-4dc1524adf71.gif) ![Allegro_Hand_400](https://user-images.githubusercontent.com/463063/125261559-38373700-e2b6-11eb-80eb-b250a0693f0b.gif) ![Shadow_Hand_OpenAI](https://user-images.githubusercontent.com/463063/125262637-328e2100-e2b7-11eb-99af-ea546a53f66a.gif) * [Dextreme](https://dextreme.org/) ![Allegro_Hand_real_world](https://user-images.githubusercontent.com/463063/216529475-3adeddea-94c3-4ac0-99db-00e7df4ba54b.gif) * [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? * 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) ![Ant_running](https://user-images.githubusercontent.com/463063/125260924-a5969800-e2b5-11eb-931c-116cc90d4bbe.gif) ![Humanoid_running](https://user-images.githubusercontent.com/463063/125266095-4edf8d00-e2ba-11eb-9c1a-4dc1524adf71.gif) ![Allegro_Hand_400](https://user-images.githubusercontent.com/463063/125261559-38373700-e2b6-11eb-80eb-b250a0693f0b.gif) ![Shadow_Hand_OpenAI](https://user-images.githubusercontent.com/463063/125262637-328e2100-e2b7-11eb-99af-ea546a53f66a.gif) * [Dextreme](https://dextreme.org/) ![Allegro_Hand_real_world](https://user-images.githubusercontent.com/463063/216529475-3adeddea-94c3-4ac0-99db-00e7df4ba54b.gif) * [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 - 1.6.0-1 - Package Spec generated