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mlech26l committed Jul 28, 2023
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Expand Up @@ -4,6 +4,9 @@ Partially Observabily Reinforcement Learning
In this guide, we will train a CfC network to solve a partially observable Markov decision process (POMDP).
In particular, we consider a partially observable version of the `HalfCheetah Mujoco environment <https://gymnasium.farama.org/environments/mujoco/half_cheetah/>`_.
Moreover, we will also evalute the trained policy when the observation is corrupted by noise to test the robustness of the learned policy.
This tutorial serves as a beginner's guide to using CfC networks in reinforcement learning,
showing how to define a custom CfC network and how to use it with `rllib <https://docs.ray.io/en/latest/rllib/index.html>`_, as well as
demonstrating the the advantages of RNNs in partially observable environments.

Code is provided for TensorFlow and relies on `ray[rllib] <https://docs.ray.io/en/latest/rllib/index.html>`_ for it's `proximal policy optimization (PPO) <https://arxiv.org/abs/1707.06347>`_
implementation and `gymnasium[mujoco] <https://gymnasium.farama.org/environments/mujoco/>`_ for the HalfCheetah environment.
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