%global _empty_manifest_terminate_build 0 Name: python-rlax Version: 0.1.5 Release: 1 Summary: A library of reinforcement learning building blocks in JAX. License: Apache 2.0 URL: https://github.com/deepmind/rlax Source0: https://mirrors.aliyun.com/pypi/web/packages/9b/49/486a4e55b1300c8f010240f29442d50afee498cef58f2fc73e8ba6cb6b19/rlax-0.1.5.tar.gz BuildArch: noarch Requires: python3-absl-py Requires: python3-chex Requires: python3-distrax Requires: python3-dm-env Requires: python3-jax Requires: python3-jaxlib Requires: python3-numpy %description # RLax ![CI status](https://github.com/deepmind/rlax/workflows/ci/badge.svg) ![docs](https://readthedocs.org/projects/rlax/badge/?version=latest) ![pypi](https://img.shields.io/pypi/v/rlax) RLax (pronounced "relax") is a library built on top of JAX that exposes useful building blocks for implementing reinforcement learning agents. Full documentation can be found at [rlax.readthedocs.io](https://rlax.readthedocs.io/en/latest/index.html). ## Installation You can install the latest released version of RLax from PyPI via: ```sh pip install rlax ``` or you can install the latest development version from GitHub: ```sh pip install git+https://github.com/deepmind/rlax.git ``` All RLax code may then be just in time compiled for different hardware (e.g. CPU, GPU, TPU) using `jax.jit`. In order to run the `examples/` you will also need to clone the repo and install the additional requirements: [optax](https://github.com/deepmind/optax), [haiku](https://github.com/deepmind/haiku), and [bsuite](https://github.com/deepmind/bsuite). ## Content The operations and functions provided are not complete algorithms, but implementations of reinforcement learning specific mathematical operations that are needed when building fully-functional agents capable of learning: * Values, including both state and action-values; * Values for Non-linear generalizations of the Bellman equations. * Return Distributions, aka distributional value functions; * General Value Functions, for cumulants other than the main reward; * Policies, via policy-gradients in both continuous and discrete action spaces. The library supports both on-policy and off-policy learning (i.e. learning from data sampled from a policy different from the agent's policy). See file-level and function-level doc-strings for the documentation of these functions and for references to the papers that introduced and/or used them. ## Usage See `examples/` for examples of using some of the functions in RLax to implement a few simple reinforcement learning agents, and demonstrate learning on BSuite's version of the Catch environment (a common unit-test for agent development in the reinforcement learning literature): Other examples of JAX reinforcement learning agents using `rlax` can be found in [bsuite](https://github.com/deepmind/bsuite/tree/master/bsuite/baselines). ## Background Reinforcement learning studies the problem of a learning system (the *agent*), which must learn to interact with the universe it is embedded in (the *environment*). Agent and environment interact on discrete steps. On each step the agent selects an *action*, and is provided in return a (partial) snapshot of the state of the environment (the *observation*), and a scalar feedback signal (the *reward*). The behaviour of the agent is characterized by a probability distribution over actions, conditioned on past observations of the environment (the *policy*). The agents seeks a policy that, from any given step, maximises the discounted cumulative reward that will be collected from that point onwards (the *return*). Often the agent policy or the environment dynamics itself are stochastic. In this case the return is a random variable, and the optimal agent's policy is typically more precisely specified as a policy that maximises the expectation of the return (the *value*), under the agent's and environment's stochasticity. ## Reinforcement Learning Algorithms There are three prototypical families of reinforcement learning algorithms: 1. those that estimate the value of states and actions, and infer a policy by *inspection* (e.g. by selecting the action with highest estimated value) 2. those that learn a model of the environment (capable of predicting the observations and rewards) and infer a policy via *planning*. 3. those that parameterize a policy that can be directly *executed*, In any case, policies, values or models are just functions. In deep reinforcement learning such functions are represented by a neural network. In this setting, it is common to formulate reinforcement learning updates as differentiable pseudo-loss functions (analogously to (un-)supervised learning). Under automatic differentiation, the original update rule is recovered. Note however, that in particular, the updates are only valid if the input data is sampled in the correct manner. For example, a policy gradient loss is only valid if the input trajectory is an unbiased sample from the current policy; i.e. the data are on-policy. The library cannot check or enforce such constraints. Links to papers describing how each operation is used are however provided in the functions' doc-strings. ## Naming Conventions and Developer Guidelines We define functions and operations for agents interacting with a single stream of experience. The JAX construct `vmap` can be used to apply these same functions to batches (e.g. to support *replay* and *parallel* data generation). Many functions consider policies, actions, rewards, values, in consecutive timesteps in order to compute their outputs. In this case the suffix `_t` and `tm1` is often to clarify on which step each input was generated, e.g: * `q_tm1`: the action value in the `source` state of a transition. * `a_tm1`: the action that was selected in the `source` state. * `r_t`: the resulting rewards collected in the `destination` state. * `discount_t`: the `discount` associated with a transition. * `q_t`: the action values in the `destination` state. Extensive testing is provided for each function. All tests should also verify the output of `rlax` functions when compiled to XLA using `jax.jit` and when performing batch operations using `jax.vmap`. ## Citing RLax RLax is part of the [DeepMind JAX Ecosystem], to cite RLax please use the [DeepMind JAX Ecosystem citation]. [DeepMind JAX Ecosystem]: https://deepmind.com/blog/article/using-jax-to-accelerate-our-research "DeepMind JAX Ecosystem" [DeepMind JAX Ecosystem citation]: https://github.com/deepmind/jax/blob/main/deepmind2020jax.txt "Citation" %package -n python3-rlax Summary: A library of reinforcement learning building blocks in JAX. Provides: python-rlax BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-rlax # RLax ![CI status](https://github.com/deepmind/rlax/workflows/ci/badge.svg) ![docs](https://readthedocs.org/projects/rlax/badge/?version=latest) ![pypi](https://img.shields.io/pypi/v/rlax) RLax (pronounced "relax") is a library built on top of JAX that exposes useful building blocks for implementing reinforcement learning agents. Full documentation can be found at [rlax.readthedocs.io](https://rlax.readthedocs.io/en/latest/index.html). ## Installation You can install the latest released version of RLax from PyPI via: ```sh pip install rlax ``` or you can install the latest development version from GitHub: ```sh pip install git+https://github.com/deepmind/rlax.git ``` All RLax code may then be just in time compiled for different hardware (e.g. CPU, GPU, TPU) using `jax.jit`. In order to run the `examples/` you will also need to clone the repo and install the additional requirements: [optax](https://github.com/deepmind/optax), [haiku](https://github.com/deepmind/haiku), and [bsuite](https://github.com/deepmind/bsuite). ## Content The operations and functions provided are not complete algorithms, but implementations of reinforcement learning specific mathematical operations that are needed when building fully-functional agents capable of learning: * Values, including both state and action-values; * Values for Non-linear generalizations of the Bellman equations. * Return Distributions, aka distributional value functions; * General Value Functions, for cumulants other than the main reward; * Policies, via policy-gradients in both continuous and discrete action spaces. The library supports both on-policy and off-policy learning (i.e. learning from data sampled from a policy different from the agent's policy). See file-level and function-level doc-strings for the documentation of these functions and for references to the papers that introduced and/or used them. ## Usage See `examples/` for examples of using some of the functions in RLax to implement a few simple reinforcement learning agents, and demonstrate learning on BSuite's version of the Catch environment (a common unit-test for agent development in the reinforcement learning literature): Other examples of JAX reinforcement learning agents using `rlax` can be found in [bsuite](https://github.com/deepmind/bsuite/tree/master/bsuite/baselines). ## Background Reinforcement learning studies the problem of a learning system (the *agent*), which must learn to interact with the universe it is embedded in (the *environment*). Agent and environment interact on discrete steps. On each step the agent selects an *action*, and is provided in return a (partial) snapshot of the state of the environment (the *observation*), and a scalar feedback signal (the *reward*). The behaviour of the agent is characterized by a probability distribution over actions, conditioned on past observations of the environment (the *policy*). The agents seeks a policy that, from any given step, maximises the discounted cumulative reward that will be collected from that point onwards (the *return*). Often the agent policy or the environment dynamics itself are stochastic. In this case the return is a random variable, and the optimal agent's policy is typically more precisely specified as a policy that maximises the expectation of the return (the *value*), under the agent's and environment's stochasticity. ## Reinforcement Learning Algorithms There are three prototypical families of reinforcement learning algorithms: 1. those that estimate the value of states and actions, and infer a policy by *inspection* (e.g. by selecting the action with highest estimated value) 2. those that learn a model of the environment (capable of predicting the observations and rewards) and infer a policy via *planning*. 3. those that parameterize a policy that can be directly *executed*, In any case, policies, values or models are just functions. In deep reinforcement learning such functions are represented by a neural network. In this setting, it is common to formulate reinforcement learning updates as differentiable pseudo-loss functions (analogously to (un-)supervised learning). Under automatic differentiation, the original update rule is recovered. Note however, that in particular, the updates are only valid if the input data is sampled in the correct manner. For example, a policy gradient loss is only valid if the input trajectory is an unbiased sample from the current policy; i.e. the data are on-policy. The library cannot check or enforce such constraints. Links to papers describing how each operation is used are however provided in the functions' doc-strings. ## Naming Conventions and Developer Guidelines We define functions and operations for agents interacting with a single stream of experience. The JAX construct `vmap` can be used to apply these same functions to batches (e.g. to support *replay* and *parallel* data generation). Many functions consider policies, actions, rewards, values, in consecutive timesteps in order to compute their outputs. In this case the suffix `_t` and `tm1` is often to clarify on which step each input was generated, e.g: * `q_tm1`: the action value in the `source` state of a transition. * `a_tm1`: the action that was selected in the `source` state. * `r_t`: the resulting rewards collected in the `destination` state. * `discount_t`: the `discount` associated with a transition. * `q_t`: the action values in the `destination` state. Extensive testing is provided for each function. All tests should also verify the output of `rlax` functions when compiled to XLA using `jax.jit` and when performing batch operations using `jax.vmap`. ## Citing RLax RLax is part of the [DeepMind JAX Ecosystem], to cite RLax please use the [DeepMind JAX Ecosystem citation]. [DeepMind JAX Ecosystem]: https://deepmind.com/blog/article/using-jax-to-accelerate-our-research "DeepMind JAX Ecosystem" [DeepMind JAX Ecosystem citation]: https://github.com/deepmind/jax/blob/main/deepmind2020jax.txt "Citation" %package help Summary: Development documents and examples for rlax Provides: python3-rlax-doc %description help # RLax ![CI status](https://github.com/deepmind/rlax/workflows/ci/badge.svg) ![docs](https://readthedocs.org/projects/rlax/badge/?version=latest) ![pypi](https://img.shields.io/pypi/v/rlax) RLax (pronounced "relax") is a library built on top of JAX that exposes useful building blocks for implementing reinforcement learning agents. Full documentation can be found at [rlax.readthedocs.io](https://rlax.readthedocs.io/en/latest/index.html). ## Installation You can install the latest released version of RLax from PyPI via: ```sh pip install rlax ``` or you can install the latest development version from GitHub: ```sh pip install git+https://github.com/deepmind/rlax.git ``` All RLax code may then be just in time compiled for different hardware (e.g. CPU, GPU, TPU) using `jax.jit`. In order to run the `examples/` you will also need to clone the repo and install the additional requirements: [optax](https://github.com/deepmind/optax), [haiku](https://github.com/deepmind/haiku), and [bsuite](https://github.com/deepmind/bsuite). ## Content The operations and functions provided are not complete algorithms, but implementations of reinforcement learning specific mathematical operations that are needed when building fully-functional agents capable of learning: * Values, including both state and action-values; * Values for Non-linear generalizations of the Bellman equations. * Return Distributions, aka distributional value functions; * General Value Functions, for cumulants other than the main reward; * Policies, via policy-gradients in both continuous and discrete action spaces. The library supports both on-policy and off-policy learning (i.e. learning from data sampled from a policy different from the agent's policy). See file-level and function-level doc-strings for the documentation of these functions and for references to the papers that introduced and/or used them. ## Usage See `examples/` for examples of using some of the functions in RLax to implement a few simple reinforcement learning agents, and demonstrate learning on BSuite's version of the Catch environment (a common unit-test for agent development in the reinforcement learning literature): Other examples of JAX reinforcement learning agents using `rlax` can be found in [bsuite](https://github.com/deepmind/bsuite/tree/master/bsuite/baselines). ## Background Reinforcement learning studies the problem of a learning system (the *agent*), which must learn to interact with the universe it is embedded in (the *environment*). Agent and environment interact on discrete steps. On each step the agent selects an *action*, and is provided in return a (partial) snapshot of the state of the environment (the *observation*), and a scalar feedback signal (the *reward*). The behaviour of the agent is characterized by a probability distribution over actions, conditioned on past observations of the environment (the *policy*). The agents seeks a policy that, from any given step, maximises the discounted cumulative reward that will be collected from that point onwards (the *return*). Often the agent policy or the environment dynamics itself are stochastic. In this case the return is a random variable, and the optimal agent's policy is typically more precisely specified as a policy that maximises the expectation of the return (the *value*), under the agent's and environment's stochasticity. ## Reinforcement Learning Algorithms There are three prototypical families of reinforcement learning algorithms: 1. those that estimate the value of states and actions, and infer a policy by *inspection* (e.g. by selecting the action with highest estimated value) 2. those that learn a model of the environment (capable of predicting the observations and rewards) and infer a policy via *planning*. 3. those that parameterize a policy that can be directly *executed*, In any case, policies, values or models are just functions. In deep reinforcement learning such functions are represented by a neural network. In this setting, it is common to formulate reinforcement learning updates as differentiable pseudo-loss functions (analogously to (un-)supervised learning). Under automatic differentiation, the original update rule is recovered. Note however, that in particular, the updates are only valid if the input data is sampled in the correct manner. For example, a policy gradient loss is only valid if the input trajectory is an unbiased sample from the current policy; i.e. the data are on-policy. The library cannot check or enforce such constraints. Links to papers describing how each operation is used are however provided in the functions' doc-strings. ## Naming Conventions and Developer Guidelines We define functions and operations for agents interacting with a single stream of experience. The JAX construct `vmap` can be used to apply these same functions to batches (e.g. to support *replay* and *parallel* data generation). Many functions consider policies, actions, rewards, values, in consecutive timesteps in order to compute their outputs. In this case the suffix `_t` and `tm1` is often to clarify on which step each input was generated, e.g: * `q_tm1`: the action value in the `source` state of a transition. * `a_tm1`: the action that was selected in the `source` state. * `r_t`: the resulting rewards collected in the `destination` state. * `discount_t`: the `discount` associated with a transition. * `q_t`: the action values in the `destination` state. Extensive testing is provided for each function. All tests should also verify the output of `rlax` functions when compiled to XLA using `jax.jit` and when performing batch operations using `jax.vmap`. ## Citing RLax RLax is part of the [DeepMind JAX Ecosystem], to cite RLax please use the [DeepMind JAX Ecosystem citation]. [DeepMind JAX Ecosystem]: https://deepmind.com/blog/article/using-jax-to-accelerate-our-research "DeepMind JAX Ecosystem" [DeepMind JAX Ecosystem citation]: https://github.com/deepmind/jax/blob/main/deepmind2020jax.txt "Citation" %prep %autosetup -n rlax-0.1.5 %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-rlax -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 0.1.5-1 - Package Spec generated