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[RLlib; docs] Re-organize algorithms so TOC matches README. (#26339)

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Christy Bergman 2022-07-13 01:46:36 -07:00 • committed by GitHub
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doc/source/rllib/rllib-algorithms.rst
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@ -66,7 +66,7 @@ Offline RL:
- `Importance Sampling and Weighted Importance Sampling (OPE) <https://docs.ray.io/en/latest/rllib/rllib-offline.html#is>`__ - `Importance Sampling and Weighted Importance Sampling (OPE) <https://docs.ray.io/en/latest/rllib/rllib-offline.html#is>`__
- `Monotonic Advantage Re-Weighted Imitation Learning (MARWIL) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#marwil>`__ - `Monotonic Advantage Re-Weighted Imitation Learning (MARWIL) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#marwil>`__
Model-free On-policy RL (for Games): Model-free On-policy RL:
- `Synchronous Proximal Policy Optimization (APPO) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#appo>`__ - `Synchronous Proximal Policy Optimization (APPO) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#appo>`__
- `Decentralized Distributed Proximal Policy Optimization (DD-PPO) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#ddppo>`__ - `Decentralized Distributed Proximal Policy Optimization (DD-PPO) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#ddppo>`__
@ -105,7 +105,6 @@ Bandits:
Multi-agent: Multi-agent:
- `Single-Player Alpha Zero (AlphaZero) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#alphazero>`__
- `Parameter Sharing <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#parameter>`__ - `Parameter Sharing <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#parameter>`__
- `QMIX Monotonic Value Factorisation (QMIX, VDN, IQN)) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#qmix>`__ - `QMIX Monotonic Value Factorisation (QMIX, VDN, IQN)) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#qmix>`__
- `Multi-Agent Deep Deterministic Policy Gradient (MADDPG) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#maddpg>`__ - `Multi-Agent Deep Deterministic Policy Gradient (MADDPG) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#maddpg>`__
@ -113,6 +112,7 @@ Multi-agent:
Others: Others:
- `Single-Player Alpha Zero (AlphaZero) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#alphazero>`__
- `Curiosity (ICM: Intrinsic Curiosity Module) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#curiosity>`__ - `Curiosity (ICM: Intrinsic Curiosity Module) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#curiosity>`__
- `Random encoders (contrib/RE3) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#re3>`__ - `Random encoders (contrib/RE3) <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#re3>`__
- `Fully Independent Learning <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#fil>`__ - `Fully Independent Learning <https://docs.ray.io/en/master/rllib/rllib-algorithms.html#fil>`__