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[rllib] RLlib in 60 seconds documentation (#5430)
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@ -200,6 +200,7 @@ The following are good places to discuss Ray.
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:caption: RLlib
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rllib.rst
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rllib-toc.rst
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rllib-training.rst
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rllib-env.rst
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rllib-models.rst
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RLlib Table of Contents
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=======================
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Training APIs
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-------------
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* `Command-line <rllib-training.html>`__
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* `Configuration <rllib-training.html#configuration>`__
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* `Python API <rllib-training.html#python-api>`__
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* `Debugging <rllib-training.html#debugging>`__
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* `REST API <rllib-training.html#rest-api>`__
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Environments
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------------
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* `RLlib Environments Overview <rllib-env.html>`__
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* `Feature Compatibility Matrix <rllib-env.html#feature-compatibility-matrix>`__
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* `OpenAI Gym <rllib-env.html#openai-gym>`__
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* `Vectorized <rllib-env.html#vectorized>`__
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* `Multi-Agent and Hierarchical <rllib-env.html#multi-agent-and-hierarchical>`__
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* `Interfacing with External Agents <rllib-env.html#interfacing-with-external-agents>`__
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* `Advanced Integrations <rllib-env.html#advanced-integrations>`__
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Models, Preprocessors, and Action Distributions
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-----------------------------------------------
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* `RLlib Models, Preprocessors, and Action Distributions Overview <rllib-models.html>`__
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* `TensorFlow Models <rllib-models.html#tensorflow-models>`__
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* `PyTorch Models <rllib-models.html#pytorch-models>`__
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* `Custom Preprocessors <rllib-models.html#custom-preprocessors>`__
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* `Custom Action Distributions <rllib-models.html#custom-action-distributions>`__
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* `Supervised Model Losses <rllib-models.html#supervised-model-losses>`__
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* `Variable-length / Parametric Action Spaces <rllib-models.html#variable-length-parametric-action-spaces>`__
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* `Autoregressive Action Distributions <rllib-models.html#autoregressive-action-distributions>`__
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Algorithms
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----------
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* High-throughput architectures
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- `Distributed Prioritized Experience Replay (Ape-X) <rllib-algorithms.html#distributed-prioritized-experience-replay-ape-x>`__
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- `Importance Weighted Actor-Learner Architecture (IMPALA) <rllib-algorithms.html#importance-weighted-actor-learner-architecture-impala>`__
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- `Asynchronous Proximal Policy Optimization (APPO) <rllib-algorithms.html#asynchronous-proximal-policy-optimization-appo>`__
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* Gradient-based
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- `Advantage Actor-Critic (A2C, A3C) <rllib-algorithms.html#advantage-actor-critic-a2c-a3c>`__
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- `Deep Deterministic Policy Gradients (DDPG, TD3) <rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg-td3>`__
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- `Deep Q Networks (DQN, Rainbow, Parametric DQN) <rllib-algorithms.html#deep-q-networks-dqn-rainbow-parametric-dqn>`__
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- `Policy Gradients <rllib-algorithms.html#policy-gradients>`__
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- `Proximal Policy Optimization (PPO) <rllib-algorithms.html#proximal-policy-optimization-ppo>`__
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- `Soft Actor Critic (SAC) <rllib-algorithms.html#soft-actor-critic-sac>`__
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* Derivative-free
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- `Augmented Random Search (ARS) <rllib-algorithms.html#augmented-random-search-ars>`__
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- `Evolution Strategies <rllib-algorithms.html#evolution-strategies>`__
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* Multi-agent specific
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- `QMIX Monotonic Value Factorisation (QMIX, VDN, IQN) <rllib-algorithms.html#qmix-monotonic-value-factorisation-qmix-vdn-iqn>`__
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- `Multi-Agent Deep Deterministic Policy Gradient (contrib/MADDPG) <rllib-algorithms.html#multi-agent-deep-deterministic-policy-gradient-contrib-maddpg>`__
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* Offline
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- `Advantage Re-Weighted Imitation Learning (MARWIL) <rllib-algorithms.html#advantage-re-weighted-imitation-learning-marwil>`__
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Offline Datasets
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----------------
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* `Working with Offline Datasets <rllib-offline.html>`__
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* `Input Pipeline for Supervised Losses <rllib-offline.html#input-pipeline-for-supervised-losses>`__
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* `Input API <rllib-offline.html#input-api>`__
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* `Output API <rllib-offline.html#output-api>`__
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Concepts and Custom Algorithms
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------------------------------
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* `Policies <rllib-concepts.html>`__
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- `Policies in Multi-Agent <rllib-concepts.html#policies-in-multi-agent>`__
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- `Building Policies in TensorFlow <rllib-concepts.html#building-policies-in-tensorflow>`__
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- `Building Policies in TensorFlow Eager <rllib-concepts.html#building-policies-in-tensorflow-eager>`__
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- `Building Policies in PyTorch <rllib-concepts.html#building-policies-in-pytorch>`__
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- `Extending Existing Policies <rllib-concepts.html#extending-existing-policies>`__
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* `Policy Evaluation <rllib-concepts.html#policy-evaluation>`__
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* `Policy Optimization <rllib-concepts.html#policy-optimization>`__
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* `Trainers <rllib-concepts.html#trainers>`__
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Examples
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--------
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* `Tuned Examples <rllib-examples.html#tuned-examples>`__
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* `Training Workflows <rllib-examples.html#training-workflows>`__
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* `Custom Envs and Models <rllib-examples.html#custom-envs-and-models>`__
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* `Serving and Offline <rllib-examples.html#serving-and-offline>`__
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* `Multi-Agent and Hierarchical <rllib-examples.html#multi-agent-and-hierarchical>`__
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* `Community Examples <rllib-examples.html#community-examples>`__
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Development
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-----------
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* `Development Install <rllib-dev.html#development-install>`__
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* `API Stability <rllib-dev.html#api-stability>`__
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* `Features <rllib-dev.html#feature-development>`__
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* `Benchmarks <rllib-dev.html#benchmarks>`__
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* `Contributing Algorithms <rllib-dev.html#contributing-algorithms>`__
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Package Reference
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-----------------
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* `ray.rllib.agents <rllib-package-ref.html#module-ray.rllib.agents>`__
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* `ray.rllib.env <rllib-package-ref.html#module-ray.rllib.env>`__
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* `ray.rllib.evaluation <rllib-package-ref.html#module-ray.rllib.evaluation>`__
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* `ray.rllib.models <rllib-package-ref.html#module-ray.rllib.models>`__
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* `ray.rllib.optimizers <rllib-package-ref.html#module-ray.rllib.optimizers>`__
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* `ray.rllib.utils <rllib-package-ref.html#module-ray.rllib.utils>`__
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Troubleshooting
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---------------
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If you encounter errors like
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`blas_thread_init: pthread_create: Resource temporarily unavailable` when using many workers,
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try setting ``OMP_NUM_THREADS=1``. Similarly, check configured system limits with
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`ulimit -a` for other resource limit errors.
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If you encounter out-of-memory errors, consider setting ``redis_max_memory`` and ``object_store_memory`` in ``ray.init()`` to reduce memory usage.
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For debugging unexpected hangs or performance problems, you can run ``ray stack`` to dump
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the stack traces of all Ray workers on the current node, and ``ray timeline`` to dump
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a timeline visualization of tasks to a file.
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@ -7,155 +7,109 @@ RLlib is an open-source library for reinforcement learning that offers both high
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To get started, take a look over the `custom env example <https://github.com/ray-project/ray/blob/master/rllib/examples/custom_env.py>`__ and the `API documentation <rllib-training.html>`__. If you're looking to develop custom algorithms with RLlib, also check out `concepts and custom algorithms <rllib-concepts.html>`__.
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Installation
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------------
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RLlib in 60 seconds
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-------------------
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The following is a whirlwind overview of RLlib. See also the full `table of contents <rllib-toc.html>`__ for a more in-depth guide including the `list of built-in algorithms <rllib-toc.html#algorithms>`__.
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Running RLlib
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~~~~~~~~~~~~~
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RLlib has extra dependencies on top of ``ray``. First, you'll need to install either `PyTorch <http://pytorch.org/>`__ or `TensorFlow <https://www.tensorflow.org>`__. Then, install the RLlib module:
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.. code-block:: bash
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pip install tensorflow # or tensorflow-gpu
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pip install ray[rllib] # also recommended: ray[debug]
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You might also want to clone the `Ray repo <https://github.com/ray-project/ray>`__ for convenient access to RLlib helper scripts:
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Then, you can try out training in the following equivalent ways:
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.. code-block:: bash
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git clone https://github.com/ray-project/ray
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cd ray/rllib
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rllib train --run=PPO --env=CartPole-v0
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Training APIs
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-------------
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* `Command-line <rllib-training.html>`__
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* `Configuration <rllib-training.html#configuration>`__
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* `Python API <rllib-training.html#python-api>`__
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* `Debugging <rllib-training.html#debugging>`__
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* `REST API <rllib-training.html#rest-api>`__
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.. code-block:: python
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Environments
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------------
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* `RLlib Environments Overview <rllib-env.html>`__
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* `Feature Compatibility Matrix <rllib-env.html#feature-compatibility-matrix>`__
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* `OpenAI Gym <rllib-env.html#openai-gym>`__
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* `Vectorized <rllib-env.html#vectorized>`__
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* `Multi-Agent and Hierarchical <rllib-env.html#multi-agent-and-hierarchical>`__
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* `Interfacing with External Agents <rllib-env.html#interfacing-with-external-agents>`__
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* `Advanced Integrations <rllib-env.html#advanced-integrations>`__
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from ray import tune
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from ray.rllib.agents.ppo import PPOTrainer
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tune.run(PPOTrainer, config={"env": "CartPole-v0"})
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Models, Preprocessors, and Action Distributions
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-----------------------------------------------
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* `RLlib Models, Preprocessors, and Action Distributions Overview <rllib-models.html>`__
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* `TensorFlow Models <rllib-models.html#tensorflow-models>`__
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* `PyTorch Models <rllib-models.html#pytorch-models>`__
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* `Custom Preprocessors <rllib-models.html#custom-preprocessors>`__
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* `Custom Action Distributions <rllib-models.html#custom-action-distributions>`__
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* `Supervised Model Losses <rllib-models.html#supervised-model-losses>`__
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* `Variable-length / Parametric Action Spaces <rllib-models.html#variable-length-parametric-action-spaces>`__
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* `Autoregressive Action Distributions <rllib-models.html#autoregressive-action-distributions>`__
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Next, we'll cover three key concepts in RLlib: Policies, Samples, and Trainers.
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Algorithms
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----------
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Policies
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~~~~~~~~
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* High-throughput architectures
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`Policies <rllib-concepts.html#policies>`__ are a core concept in RLlib. In a nutshell, policies are Python classes that define how an agent acts in an environment. `Rollout workers <rllib-concepts.html#policy-evaluation>`__ query the policy to determine agent actions. In a `gym <rllib-env.html#openai-gym>`__ environment, there is a single agent and policy. In `vector envs <rllib-env.html#vectorized>`__, policy inference is for multiple agents at once, and in `multi-agent <rllib-env.html#multi-agent-and-hierarchical>`__, there may be multiple policies, each controlling one or more agents:
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- `Distributed Prioritized Experience Replay (Ape-X) <rllib-algorithms.html#distributed-prioritized-experience-replay-ape-x>`__
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.. image:: multi-flat.svg
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- `Importance Weighted Actor-Learner Architecture (IMPALA) <rllib-algorithms.html#importance-weighted-actor-learner-architecture-impala>`__
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Policies can be implemented using `any framework <https://github.com/ray-project/ray/blob/master/rllib/policy/policy.py>`__. However, for TensorFlow and PyTorch, RLlib has `build_tf_policy <rllib-concepts.html#building-policies-in-tensorflow>`__ and `build_torch_policy <rllib-concepts.html#building-policies-in-pytorch>`__ helper functions that let you define a trainable policy with a functional-style API, for example:
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- `Asynchronous Proximal Policy Optimization (APPO) <rllib-algorithms.html#asynchronous-proximal-policy-optimization-appo>`__
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.. code-block:: python
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* Gradient-based
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def policy_gradient_loss(policy, batch_tensors):
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actions = batch_tensors[SampleBatch.ACTIONS]
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rewards = batch_tensors[SampleBatch.REWARDS]
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return -tf.reduce_mean(policy.action_dist.logp(actions) * rewards)
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- `Advantage Actor-Critic (A2C, A3C) <rllib-algorithms.html#advantage-actor-critic-a2c-a3c>`__
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# <class 'ray.rllib.policy.tf_policy_template.MyTFPolicy'>
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MyTFPolicy = build_tf_policy(
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name="MyTFPolicy",
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loss_fn=policy_gradient_loss)
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- `Deep Deterministic Policy Gradients (DDPG, TD3) <rllib-algorithms.html#deep-deterministic-policy-gradients-ddpg-td3>`__
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Sample Batches
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~~~~~~~~~~~~~~
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- `Deep Q Networks (DQN, Rainbow, Parametric DQN) <rllib-algorithms.html#deep-q-networks-dqn-rainbow-parametric-dqn>`__
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Whether running in a single process or `large cluster <rllib-training.html#specifying-resources>`__, all data interchange in RLlib is in the form of `sample batches <https://github.com/ray-project/ray/blob/master/rllib/policy/sample_batch.py>`__. Sample batches encode one or more fragments of a trajectory. Typically, RLlib collects batches of size ``sample_batch_size`` from rollout workers, and concatenates one or more of these batches into a batch of size ``train_batch_size`` that is the input to SGD.
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- `Policy Gradients <rllib-algorithms.html#policy-gradients>`__
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A typical sample batch looks something like the following when summarized. Since all values are kept in arrays, this allows for efficient encoding and transmission across the network:
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- `Proximal Policy Optimization (PPO) <rllib-algorithms.html#proximal-policy-optimization-ppo>`__
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.. code-block:: python
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- `Soft Actor Critic (SAC) <rllib-algorithms.html#soft-actor-critic-sac>`__
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{ 'action_logp': np.ndarray((200,), dtype=float32, min=-0.701, max=-0.685, mean=-0.694),
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'actions': np.ndarray((200,), dtype=int64, min=0.0, max=1.0, mean=0.495),
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'dones': np.ndarray((200,), dtype=bool, min=0.0, max=1.0, mean=0.055),
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'infos': np.ndarray((200,), dtype=object, head={}),
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'new_obs': np.ndarray((200, 4), dtype=float32, min=-2.46, max=2.259, mean=0.018),
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'obs': np.ndarray((200, 4), dtype=float32, min=-2.46, max=2.259, mean=0.016),
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'rewards': np.ndarray((200,), dtype=float32, min=1.0, max=1.0, mean=1.0),
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't': np.ndarray((200,), dtype=int64, min=0.0, max=34.0, mean=9.14)}
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* Derivative-free
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In `multi-agent mode <rllib-concepts.html#policies-in-multi-agent>`__, sample batches are collected separately for each individual policy.
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- `Augmented Random Search (ARS) <rllib-algorithms.html#augmented-random-search-ars>`__
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Training
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~~~~~~~~
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- `Evolution Strategies <rllib-algorithms.html#evolution-strategies>`__
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Policies each define a ``learn_on_batch()`` method that improves the policy given a sample batch of input. For TF and Torch policies, this is implemented using a `loss function` that takes as input sample batch tensors and outputs a scalar loss. Here are a few example loss functions:
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* Multi-agent specific
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- Simple `policy gradient loss <https://github.com/ray-project/ray/blob/master/rllib/agents/pg/pg_policy.py>`__
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- Simple `Q-function loss <https://github.com/ray-project/ray/blob/a1d2e1762325cd34e14dc411666d63bb15d6eaf0/rllib/agents/dqn/simple_q_policy.py#L136>`__
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- Importance-weighted `APPO surrogate loss <https://github.com/ray-project/ray/blob/master/rllib/agents/ppo/appo_policy.py>`__
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- `QMIX Monotonic Value Factorisation (QMIX, VDN, IQN) <rllib-algorithms.html#qmix-monotonic-value-factorisation-qmix-vdn-iqn>`__
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- `Multi-Agent Deep Deterministic Policy Gradient (contrib/MADDPG) <rllib-algorithms.html#multi-agent-deep-deterministic-policy-gradient-contrib-maddpg>`__
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RLlib `Trainer classes <rllib-concepts.html#trainers>`__ coordinate the distributed workflow of running rollouts and optimizing policies. They do this by leveraging `policy optimizers <rllib-concepts.html#policy-optimization>`__ that implement the desired computation pattern (i.e., synchronous or asynchronous sampling, distributed replay, etc):
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* Offline
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.. figure:: a2c-arch.svg
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- `Advantage Re-Weighted Imitation Learning (MARWIL) <rllib-algorithms.html#advantage-re-weighted-imitation-learning-marwil>`__
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Synchronous Sampling (e.g., A2C, PG, PPO)
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Offline Datasets
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----------------
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* `Working with Offline Datasets <rllib-offline.html>`__
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* `Input Pipeline for Supervised Losses <rllib-offline.html#input-pipeline-for-supervised-losses>`__
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* `Input API <rllib-offline.html#input-api>`__
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* `Output API <rllib-offline.html#output-api>`__
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.. figure:: dqn-arch.svg
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Concepts and Custom Algorithms
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------------------------------
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* `Policies <rllib-concepts.html>`__
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Synchronous Replay (e.g., DQN, DDPG, TD3)
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- `Policies in Multi-Agent <rllib-concepts.html#policies-in-multi-agent>`__
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.. figure:: impala-arch.svg
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- `Building Policies in TensorFlow <rllib-concepts.html#building-policies-in-tensorflow>`__
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Asynchronous Sampling (e.g., IMPALA, APPO)
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- `Building Policies in TensorFlow Eager <rllib-concepts.html#building-policies-in-tensorflow-eager>`__
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.. figure:: apex-arch.svg
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- `Building Policies in PyTorch <rllib-concepts.html#building-policies-in-pytorch>`__
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Asynchronous Replay (e.g., Ape-X)
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- `Extending Existing Policies <rllib-concepts.html#extending-existing-policies>`__
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RLlib uses `Ray actors <actors.html>`__ to scale these architectures from a single core to many thousands of cores in a cluster. You can `configure the parallelism <rllib-training.html#specifying-resources>`__ used for training by changing the ``num_workers`` parameter.
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* `Policy Evaluation <rllib-concepts.html#policy-evaluation>`__
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* `Policy Optimization <rllib-concepts.html#policy-optimization>`__
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* `Trainers <rllib-concepts.html#trainers>`__
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Customization
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~~~~~~~~~~~~~
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Examples
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||||
--------
|
||||
RLlib provides ways to customize almost all aspects of training, including the `environment <rllib-env.html#configuring-environments>`__, `neural network model <rllib-models.html#tensorflow-models>`__, `action distribution <rllib-models.html#custom-action-distributions>`__, and `policy definitions <rllib-concepts.html#policies>`__:
|
||||
|
||||
* `Tuned Examples <rllib-examples.html#tuned-examples>`__
|
||||
* `Training Workflows <rllib-examples.html#training-workflows>`__
|
||||
* `Custom Envs and Models <rllib-examples.html#custom-envs-and-models>`__
|
||||
* `Serving and Offline <rllib-examples.html#serving-and-offline>`__
|
||||
* `Multi-Agent and Hierarchical <rllib-examples.html#multi-agent-and-hierarchical>`__
|
||||
* `Community Examples <rllib-examples.html#community-examples>`__
|
||||
.. image:: rllib-components.svg
|
||||
|
||||
Development
|
||||
-----------
|
||||
|
||||
* `Development Install <rllib-dev.html#development-install>`__
|
||||
* `API Stability <rllib-dev.html#api-stability>`__
|
||||
* `Features <rllib-dev.html#feature-development>`__
|
||||
* `Benchmarks <rllib-dev.html#benchmarks>`__
|
||||
* `Contributing Algorithms <rllib-dev.html#contributing-algorithms>`__
|
||||
|
||||
Package Reference
|
||||
-----------------
|
||||
* `ray.rllib.agents <rllib-package-ref.html#module-ray.rllib.agents>`__
|
||||
* `ray.rllib.env <rllib-package-ref.html#module-ray.rllib.env>`__
|
||||
* `ray.rllib.evaluation <rllib-package-ref.html#module-ray.rllib.evaluation>`__
|
||||
* `ray.rllib.models <rllib-package-ref.html#module-ray.rllib.models>`__
|
||||
* `ray.rllib.optimizers <rllib-package-ref.html#module-ray.rllib.optimizers>`__
|
||||
* `ray.rllib.utils <rllib-package-ref.html#module-ray.rllib.utils>`__
|
||||
|
||||
Troubleshooting
|
||||
---------------
|
||||
|
||||
If you encounter errors like
|
||||
`blas_thread_init: pthread_create: Resource temporarily unavailable` when using many workers,
|
||||
try setting ``OMP_NUM_THREADS=1``. Similarly, check configured system limits with
|
||||
`ulimit -a` for other resource limit errors.
|
||||
|
||||
If you encounter out-of-memory errors, consider setting ``redis_max_memory`` and ``object_store_memory`` in ``ray.init()`` to reduce memory usage.
|
||||
|
||||
For debugging unexpected hangs or performance problems, you can run ``ray stack`` to dump
|
||||
the stack traces of all Ray workers on the current node, and ``ray timeline`` to dump
|
||||
a timeline visualization of tasks to a file.
|
||||
To learn more, proceed to the `table of contents <rllib-toc.html>`__.
|
||||
|
|
|
|||
|
|
@ -14,77 +14,6 @@ from ray.rllib.utils.memory import concat_aligned
|
|||
DEFAULT_POLICY_ID = "default_policy"
|
||||
|
||||
|
||||
@PublicAPI
|
||||
class MultiAgentBatch(object):
|
||||
"""A batch of experiences from multiple policies in the environment.
|
||||
|
||||
Attributes:
|
||||
policy_batches (dict): Mapping from policy id to a normal SampleBatch
|
||||
of experiences. Note that these batches may be of different length.
|
||||
count (int): The number of timesteps in the environment this batch
|
||||
contains. This will be less than the number of transitions this
|
||||
batch contains across all policies in total.
|
||||
"""
|
||||
|
||||
@PublicAPI
|
||||
def __init__(self, policy_batches, count):
|
||||
self.policy_batches = policy_batches
|
||||
self.count = count
|
||||
|
||||
@staticmethod
|
||||
@PublicAPI
|
||||
def wrap_as_needed(batches, count):
|
||||
if len(batches) == 1 and DEFAULT_POLICY_ID in batches:
|
||||
return batches[DEFAULT_POLICY_ID]
|
||||
return MultiAgentBatch(batches, count)
|
||||
|
||||
@staticmethod
|
||||
@PublicAPI
|
||||
def concat_samples(samples):
|
||||
policy_batches = collections.defaultdict(list)
|
||||
total_count = 0
|
||||
for s in samples:
|
||||
assert isinstance(s, MultiAgentBatch)
|
||||
for policy_id, batch in s.policy_batches.items():
|
||||
policy_batches[policy_id].append(batch)
|
||||
total_count += s.count
|
||||
out = {}
|
||||
for policy_id, batches in policy_batches.items():
|
||||
out[policy_id] = SampleBatch.concat_samples(batches)
|
||||
return MultiAgentBatch(out, total_count)
|
||||
|
||||
@PublicAPI
|
||||
def copy(self):
|
||||
return MultiAgentBatch(
|
||||
{k: v.copy()
|
||||
for (k, v) in self.policy_batches.items()}, self.count)
|
||||
|
||||
@PublicAPI
|
||||
def total(self):
|
||||
ct = 0
|
||||
for batch in self.policy_batches.values():
|
||||
ct += batch.count
|
||||
return ct
|
||||
|
||||
@DeveloperAPI
|
||||
def compress(self, bulk=False, columns=frozenset(["obs", "new_obs"])):
|
||||
for batch in self.policy_batches.values():
|
||||
batch.compress(bulk=bulk, columns=columns)
|
||||
|
||||
@DeveloperAPI
|
||||
def decompress_if_needed(self, columns=frozenset(["obs", "new_obs"])):
|
||||
for batch in self.policy_batches.values():
|
||||
batch.decompress_if_needed(columns)
|
||||
|
||||
def __str__(self):
|
||||
return "MultiAgentBatch({}, count={})".format(
|
||||
str(self.policy_batches), self.count)
|
||||
|
||||
def __repr__(self):
|
||||
return "MultiAgentBatch({}, count={})".format(
|
||||
str(self.policy_batches), self.count)
|
||||
|
||||
|
||||
@PublicAPI
|
||||
class SampleBatch(object):
|
||||
"""Wrapper around a dictionary with string keys and array-like values.
|
||||
|
|
@ -294,3 +223,74 @@ class SampleBatch(object):
|
|||
|
||||
def __contains__(self, x):
|
||||
return x in self.data
|
||||
|
||||
|
||||
@PublicAPI
|
||||
class MultiAgentBatch(object):
|
||||
"""A batch of experiences from multiple policies in the environment.
|
||||
|
||||
Attributes:
|
||||
policy_batches (dict): Mapping from policy id to a normal SampleBatch
|
||||
of experiences. Note that these batches may be of different length.
|
||||
count (int): The number of timesteps in the environment this batch
|
||||
contains. This will be less than the number of transitions this
|
||||
batch contains across all policies in total.
|
||||
"""
|
||||
|
||||
@PublicAPI
|
||||
def __init__(self, policy_batches, count):
|
||||
self.policy_batches = policy_batches
|
||||
self.count = count
|
||||
|
||||
@staticmethod
|
||||
@PublicAPI
|
||||
def wrap_as_needed(batches, count):
|
||||
if len(batches) == 1 and DEFAULT_POLICY_ID in batches:
|
||||
return batches[DEFAULT_POLICY_ID]
|
||||
return MultiAgentBatch(batches, count)
|
||||
|
||||
@staticmethod
|
||||
@PublicAPI
|
||||
def concat_samples(samples):
|
||||
policy_batches = collections.defaultdict(list)
|
||||
total_count = 0
|
||||
for s in samples:
|
||||
assert isinstance(s, MultiAgentBatch)
|
||||
for policy_id, batch in s.policy_batches.items():
|
||||
policy_batches[policy_id].append(batch)
|
||||
total_count += s.count
|
||||
out = {}
|
||||
for policy_id, batches in policy_batches.items():
|
||||
out[policy_id] = SampleBatch.concat_samples(batches)
|
||||
return MultiAgentBatch(out, total_count)
|
||||
|
||||
@PublicAPI
|
||||
def copy(self):
|
||||
return MultiAgentBatch(
|
||||
{k: v.copy()
|
||||
for (k, v) in self.policy_batches.items()}, self.count)
|
||||
|
||||
@PublicAPI
|
||||
def total(self):
|
||||
ct = 0
|
||||
for batch in self.policy_batches.values():
|
||||
ct += batch.count
|
||||
return ct
|
||||
|
||||
@DeveloperAPI
|
||||
def compress(self, bulk=False, columns=frozenset(["obs", "new_obs"])):
|
||||
for batch in self.policy_batches.values():
|
||||
batch.compress(bulk=bulk, columns=columns)
|
||||
|
||||
@DeveloperAPI
|
||||
def decompress_if_needed(self, columns=frozenset(["obs", "new_obs"])):
|
||||
for batch in self.policy_batches.values():
|
||||
batch.decompress_if_needed(columns)
|
||||
|
||||
def __str__(self):
|
||||
return "MultiAgentBatch({}, count={})".format(
|
||||
str(self.policy_batches), self.count)
|
||||
|
||||
def __repr__(self):
|
||||
return "MultiAgentBatch({}, count={})".format(
|
||||
str(self.policy_batches), self.count)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue