From b63315b979d55e49ee655d40349a2fab83f245b5 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Fri, 25 Feb 2022 22:00:52 -0500 Subject: [PATCH] Add ppo.py documetnation (#120) * Add PPO documetnation * Add test * Update docs * refactor * Update documentation * update docs * Quick fix --- README.md | 24 +++- benchmark/ppo/README.md | 38 ++++++ .../classic_control_separate_networks.sh | 84 ++++++++++++ benchmark/ppo/cleanrl/classic_control.sh | 9 ++ cleanrl/ppo.py | 3 + docs/index.md | 2 +- docs/rl-algorithms/overview.md | 18 +++ docs/rl-algorithms/ppo.md | 125 +++++++++++++++--- docs/rl-algorithms/ppo/Acrobot-v1.png | Bin 0 -> 1093212 bytes docs/rl-algorithms/ppo/CartPole-v1.png | Bin 0 -> 1312233 bytes docs/rl-algorithms/ppo/MountainCar-v0.png | Bin 0 -> 384558 bytes docs/rl-algorithms/{ => ppo}/ppo-1-title.png | Bin docs/rl-algorithms/{ => ppo}/ppo-2-title.png | Bin docs/rl-algorithms/{ => ppo}/ppo-3-title.png | Bin docs/stylesheets/extra.css | 6 + mkdocs.yml | 4 + 16 files changed, 283 insertions(+), 30 deletions(-) create mode 100644 benchmark/ppo/README.md create mode 100644 benchmark/ppo/baselines/classic_control_separate_networks.sh create mode 100644 benchmark/ppo/cleanrl/classic_control.sh create mode 100644 docs/rl-algorithms/overview.md create mode 100644 docs/rl-algorithms/ppo/Acrobot-v1.png create mode 100644 docs/rl-algorithms/ppo/CartPole-v1.png create mode 100644 docs/rl-algorithms/ppo/MountainCar-v0.png rename docs/rl-algorithms/{ => ppo}/ppo-1-title.png (100%) rename docs/rl-algorithms/{ => ppo}/ppo-2-title.png (100%) rename docs/rl-algorithms/{ => ppo}/ppo-3-title.png (100%) diff --git a/README.md b/README.md index 04a902744..e966e6c24 100644 --- a/README.md +++ b/README.md @@ -108,13 +108,23 @@ You may also use a prebuilt development environment hosted in Gitpod: [![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](https://gitpod.io/#https://github.com/vwxyzjn/cleanrl) ## Algorithms Implemented -- [x] Deep Q-Learning (DQN) -- [x] Categorical DQN (C51) -- [x] Proximal Policy Gradient (PPO) -- [x] Soft Actor Critic (SAC) -- [x] Deep Deterministic Policy Gradient (DDPG) -- [x] Twin Delayed Deep Deterministic Policy Gradient (TD3) -- [x] Apex Deep Q-Learning (Apex-DQN) + +| Algorithm | Variants Implemented | +| ----------- | ----------- | +| ✅ [Proximal Policy Gradient (PPO)](https://arxiv.org/pdf/1707.06347.pdf) | [`ppo.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py), [docs](https://docs.cleanrl.dev/rl-algorithms/ppo/#ppopy) | +| | [`ppo_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari.py), [docs](https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_ataripy) | +| | [`ppo_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_continuous_action.py), [docs](https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_continuous_actionpy) | +| | [`ppo_atari_lstm.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_lstm.py) | +| | [`ppo_procgen.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_procgen.py) | +| ✅ [Deep Q-Learning (DQN)](https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf) | [`dqn.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn.py) | +| | [`dqn_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py) | +| ✅ [Categorical DQN (C51)](https://arxiv.org/pdf/1707.06887.pdf) | [`c51.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51.py) | +| | [`c51_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_atari.py) | +| ✅ [Apex Deep Q-Learning (Apex-DQN)](https://arxiv.org/pdf/1803.00933.pdf) | [`apex_dqn_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/apex_dqn_atari.py) | +| ✅ [Soft Actor-Critic (SAC)](https://arxiv.org/pdf/1812.05905.pdf) | [`sac_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/sac_continuous_action.py) | +| ✅ [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/pdf/1509.02971.pdf) | [`ddpg_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py) | +| ✅ [Twin Delayed Deep Deterministic Policy Gradient (TD3)](https://arxiv.org/pdf/1802.09477.pdf) | [`td3_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/td3_continuous_action.py) | + ## Open RL Benchmark diff --git a/benchmark/ppo/README.md b/benchmark/ppo/README.md new file mode 100644 index 000000000..6a598b608 --- /dev/null +++ b/benchmark/ppo/README.md @@ -0,0 +1,38 @@ +# Proximal Policy Optimization Benchmark + +This repository contains instructions to reproduce our PPO experiments done with CleanRL and `openai/baselines`. + +## Install CleanRL + +Prerequisites: +* Python 3.8+ +* [Poetry](https://python-poetry.org) + +Install dependencies: + +```bash +git clone https://github.com/vwxyzjn/cleanrl.git && cd cleanrl +poetry install +``` + +## Reproduce CleanRL's PPO Benchmark + +Follow the scripts at the `cleanrl` sub-folder. Note that you may need to overwrite the `--wandb-entity cleanrl` to your own W&B entity. + +```bash +# reproduce the classic control experiments +bash cleanrl/classic_control.sh +``` + +## Install `openai/baselines` + +Follow the instructions at our fork https://github.com/vwxyzjn/baselines to install. + +## Reproduce CleanRL's PPO Benchmark + +Follow the scripts at the `baselines` sub-folder. Note that you may need to overwrite the `WANDB_ENTITY=cleanrl` to your own W&B entity. + +```bash +# reproduce the classic control experiments +bash cleanrl/classic_control.sh +``` diff --git a/benchmark/ppo/baselines/classic_control_separate_networks.sh b/benchmark/ppo/baselines/classic_control_separate_networks.sh new file mode 100644 index 000000000..2586c4963 --- /dev/null +++ b/benchmark/ppo/baselines/classic_control_separate_networks.sh @@ -0,0 +1,84 @@ +# CartPole-v1 +CUDA_VISIBLE_DEVICES="-1" WANDB_PROJECT=openai-baselines-benchmark WANDB_ENTITY=cleanrl OPENAI_LOGDIR=$PWD/runs OPENAI_LOG_FORMAT=tensorboard poetry run python -m baselines.run_separate_networks \ + --alg=ppo2 \ + --num_timesteps=500000 \ + --num_env 4 \ + --env=CartPole-v1 \ + --network mlp \ + --value_network='copy' \ + --track \ + --seed 1 + +CUDA_VISIBLE_DEVICES="-1" WANDB_PROJECT=openai-baselines-benchmark WANDB_ENTITY=cleanrl OPENAI_LOGDIR=$PWD/runs OPENAI_LOG_FORMAT=tensorboard poetry run python -m baselines.run_separate_networks \ + --alg=ppo2 \ + --num_timesteps=500000 \ + --env=CartPole-v1 \ + --network mlp \ + --value_network='copy' \ + --track \ + --seed 2 + +CUDA_VISIBLE_DEVICES="-1" WANDB_PROJECT=openai-baselines-benchmark WANDB_ENTITY=cleanrl OPENAI_LOGDIR=$PWD/runs OPENAI_LOG_FORMAT=tensorboard poetry run python -m baselines.run_separate_networks \ + --alg=ppo2 \ + --num_timesteps=500000 \ + --env=CartPole-v1 \ + --network mlp \ + --value_network='copy' \ + --track \ + --seed 3 + +# Acrobot-v1 +CUDA_VISIBLE_DEVICES="-1" WANDB_PROJECT=openai-baselines-benchmark WANDB_ENTITY=cleanrl OPENAI_LOGDIR=$PWD/runs OPENAI_LOG_FORMAT=tensorboard poetry run python -m baselines.run_separate_networks \ + --alg=ppo2 \ + --num_timesteps=500000 \ + --env=Acrobot-v1 \ + --network mlp \ + --value_network='copy' \ + --track \ + --seed 1 + +CUDA_VISIBLE_DEVICES="-1" WANDB_PROJECT=openai-baselines-benchmark WANDB_ENTITY=cleanrl OPENAI_LOGDIR=$PWD/runs OPENAI_LOG_FORMAT=tensorboard poetry run python -m baselines.run_separate_networks \ + --alg=ppo2 \ + --num_timesteps=500000 \ + --env=Acrobot-v1 \ + --network mlp \ + --value_network='copy' \ + --track \ + --seed 2 + +CUDA_VISIBLE_DEVICES="-1" WANDB_PROJECT=openai-baselines-benchmark WANDB_ENTITY=cleanrl OPENAI_LOGDIR=$PWD/runs OPENAI_LOG_FORMAT=tensorboard poetry run python -m baselines.run_separate_networks \ + --alg=ppo2 \ + --num_timesteps=500000 \ + --env=Acrobot-v1 \ + --network mlp \ + --value_network='copy' \ + --track \ + --seed 3 + +# MountainCar-v0 +CUDA_VISIBLE_DEVICES="-1" WANDB_PROJECT=openai-baselines-benchmark WANDB_ENTITY=cleanrl OPENAI_LOGDIR=$PWD/runs OPENAI_LOG_FORMAT=tensorboard poetry run python -m baselines.run_separate_networks \ + --alg=ppo2 \ + --num_timesteps=500000 \ + --env=MountainCar-v0 \ + --network mlp \ + --value_network='copy' \ + --track \ + --seed 1 + +CUDA_VISIBLE_DEVICES="-1" WANDB_PROJECT=openai-baselines-benchmark WANDB_ENTITY=cleanrl OPENAI_LOGDIR=$PWD/runs OPENAI_LOG_FORMAT=tensorboard poetry run python -m baselines.run_separate_networks \ + --alg=ppo2 \ + --num_timesteps=500000 \ + --env=MountainCar-v0 \ + --network mlp \ + --value_network='copy' \ + --track \ + --seed 2 + +CUDA_VISIBLE_DEVICES="-1" WANDB_PROJECT=openai-baselines-benchmark WANDB_ENTITY=cleanrl OPENAI_LOGDIR=$PWD/runs OPENAI_LOG_FORMAT=tensorboard poetry run python -m baselines.run_separate_networks \ + --alg=ppo2 \ + --num_timesteps=500000 \ + --env=MountainCar-v0 \ + --network mlp \ + --value_network='copy' \ + --track \ + --seed 3 \ No newline at end of file diff --git a/benchmark/ppo/cleanrl/classic_control.sh b/benchmark/ppo/cleanrl/classic_control.sh new file mode 100644 index 000000000..a6d79db1b --- /dev/null +++ b/benchmark/ppo/cleanrl/classic_control.sh @@ -0,0 +1,9 @@ +OMP_NUM_THREADS=1 poetry run python cleanrl/ppo.py --env-id CartPole-v1 --track --capture-video --seed 1 --wandb-entity cleanrl --wandb-project-name benchmark --cuda False --total-timesteps 500000 +OMP_NUM_THREADS=1 poetry run python cleanrl/ppo.py --env-id CartPole-v1 --track --capture-video --seed 2 --wandb-entity cleanrl --wandb-project-name benchmark --cuda False --total-timesteps 500000 +OMP_NUM_THREADS=1 poetry run python cleanrl/ppo.py --env-id CartPole-v1 --track --capture-video --seed 3 --wandb-entity cleanrl --wandb-project-name benchmark --cuda False --total-timesteps 500000 +OMP_NUM_THREADS=1 poetry run python cleanrl/ppo.py --env-id Acrobot-v1 --track --capture-video --seed 1 --wandb-entity cleanrl --wandb-project-name benchmark --cuda False --total-timesteps 500000 +OMP_NUM_THREADS=1 poetry run python cleanrl/ppo.py --env-id Acrobot-v1 --track --capture-video --seed 2 --wandb-entity cleanrl --wandb-project-name benchmark --cuda False --total-timesteps 500000 +OMP_NUM_THREADS=1 poetry run python cleanrl/ppo.py --env-id Acrobot-v1 --track --capture-video --seed 3 --wandb-entity cleanrl --wandb-project-name benchmark --cuda False --total-timesteps 500000 +OMP_NUM_THREADS=1 poetry run python cleanrl/ppo.py --env-id MountainCar-v0 --track --capture-video --seed 1 --wandb-entity cleanrl --wandb-project-name benchmark --cuda False --total-timesteps 500000 +OMP_NUM_THREADS=1 poetry run python cleanrl/ppo.py --env-id MountainCar-v0 --track --capture-video --seed 2 --wandb-entity cleanrl --wandb-project-name benchmark --cuda False --total-timesteps 500000 +OMP_NUM_THREADS=1 poetry run python cleanrl/ppo.py --env-id MountainCar-v0 --track --capture-video --seed 3 --wandb-entity cleanrl --wandb-project-name benchmark --cuda False --total-timesteps 500000 \ No newline at end of file diff --git a/cleanrl/ppo.py b/cleanrl/ppo.py index 76072b72b..67f80231f 100644 --- a/cleanrl/ppo.py +++ b/cleanrl/ppo.py @@ -1,3 +1,6 @@ +# docs and experiment results can be found at +# https://docs.cleanrl.dev/rl-algorithms/ppo/#ppopy + import argparse import os import random diff --git a/docs/index.md b/docs/index.md index ec8281410..ab37832eb 100644 --- a/docs/index.md +++ b/docs/index.md @@ -26,7 +26,7 @@ CleanRL is a Deep Reinforcement Learning library that provides high-quality sing * Experiment Management with [Weights and Biases](https://wandb.ai/site) * Cloud Integration with Docker and AWS -You can read more about CleanRL in our [technical paper]((https://arxiv.org/abs/2111.08819)) and [documentation](https://docs.cleanrl.dev/). +You can read more about CleanRL in our [technical paper](https://arxiv.org/abs/2111.08819) and [documentation](https://docs.cleanrl.dev/). Good luck have fun 🚀 diff --git a/docs/rl-algorithms/overview.md b/docs/rl-algorithms/overview.md new file mode 100644 index 000000000..69dc3c505 --- /dev/null +++ b/docs/rl-algorithms/overview.md @@ -0,0 +1,18 @@ +# Overview + +| Algorithm | Variants Implemented | +| ----------- | ----------- | +| ✅ [Proximal Policy Gradient (PPO)](https://arxiv.org/pdf/1707.06347.pdf) | :material-github: [`ppo.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py), :material-file-document: [docs](/rl-algorithms/ppo/#ppopy) | +| | :material-github: [`ppo_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari.py), :material-file-document: [docs](/rl-algorithms/ppo/#ppo_ataripy) | +| | :material-github: [`ppo_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_continuous_action.py), :material-file-document: [docs](/rl-algorithms/ppo/#ppo_continuous_actionpy) | +| | :material-github: [`ppo_atari_lstm.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_lstm.py) | +| | :material-github: [`ppo_procgen.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_procgen.py) | +| ✅ [Deep Q-Learning (DQN)](https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf) | :material-github: [`dqn.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn.py) | +| | :material-github: [`dqn_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py) | +| ✅ [Categorical DQN (C51)](https://arxiv.org/pdf/1707.06887.pdf) | :material-github: [`c51.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51.py) | +| | :material-github: [`c51_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_atari.py) | +| ✅ [Apex Deep Q-Learning (Apex-DQN)](https://arxiv.org/pdf/1803.00933.pdf) | :material-github: [`apex_dqn_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/apex_dqn_atari.py) | +| ✅ [Soft Actor-Critic (SAC)](https://arxiv.org/pdf/1812.05905.pdf) | :material-github: [`sac_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/sac_continuous_action.py) | +| ✅ [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/pdf/1509.02971.pdf) | :material-github: [`ddpg_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py) | +| ✅ [Twin Delayed Deep Deterministic Policy Gradient (TD3)](https://arxiv.org/pdf/1802.09477.pdf) | :material-github: [`td3_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/td3_continuous_action.py) | + diff --git a/docs/rl-algorithms/ppo.md b/docs/rl-algorithms/ppo.md index 40eaae011..dace1b989 100644 --- a/docs/rl-algorithms/ppo.md +++ b/docs/rl-algorithms/ppo.md @@ -1,6 +1,9 @@ -# Proximal Policy Gradient (PPO) +# Proximal Policy Gradient (PPO) -PPO is one of the most popular DRL algorithms. It runs reasonably fast by leveraging vector (parallel) environments, and naturally works well with different action spaces, therefore supporting a variety of games. It also has good sample efficiency compared to algorithms such as DQN. + +## Overview + +PPO is one of the most popular DRL algorithms. It runs reasonably fast by leveraging vector (parallel) environments and naturally works well with different action spaces, therefore supporting a variety of games. It also has good sample efficiency compared to algorithms such as DQN. Original paper: @@ -14,23 +17,101 @@ Reference resources: All our PPO implementations below are augmented with the same code-level optimizations presented in `openai/baselines`'s [PPO](https://github.com/openai/baselines/tree/master/baselines/ppo2). See [The 32 Implementation Details of Proximal Policy Optimization (PPO) Algorithm](https://costa.sh/blog-the-32-implementation-details-of-ppo.html) for more details. -Our single-file implementations of PPO: - -* [ppo.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py) - * Works with the `Box` observation space of low-level features - * Works with the `Discerete` action space - * Works with envs like `CartPole-v1` - * Includes the 11 core implementation details as shown in the following video tutorial - [![PPO1](ppo-1-title.png)](https://youtu.be/MEt6rrxH8W4) -* [ppo_atari.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari.py) - * For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques. - * Works with the Atari's pixel `Box` observation space of shape `(210, 160, 3)` - * Works with the `Discerete` action space - * Includes the 9 Atari-specific implementation details as shown in the following video tutorial - [![PPO2](ppo-2-title.png)](https://youtu.be/05RMTj-2K_Y) -* [ppo_continuous_action.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_continuous_action.py) - * For continuous action space. Also implemented Mujoco-specific code-level optimizations - * Works with the `Box` observation space of low-level features - * Works with the `Box` (continuous) action space - * Includes the 8 implementation details for as shown in the following video tutorial (need fixing) - [![PPO3](ppo-3-title.png)](https://youtu.be/05RMTj-2K_Y) +## Implemented Variants + + +| Variants Implemented | Description | +| ----------- | ----------- | +| :material-github: [`ppo.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py), :material-file-document: [docs](/rl-algorithms/ppo/#ppopy) | For classic control tasks like `CartPole-v1`. | +| :material-github: [`ppo_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari.py), :material-file-document: [docs](/rl-algorithms/ppo/#ppo_ataripy) | For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques. | +| :material-github: [`ppo_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_continuous_action.py), :material-file-document: [docs](/rl-algorithms/ppo/#ppo_continuous_actionpy) | For continuous action space. Also implemented Mujoco-specific code-level optimizations | + + +Below are our single-file implementations of PPO: + +## `ppo.py` + +The [ppo.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py) has the following features: + +* Works with the `Box` observation space of low-level features +* Works with the `Discerete` action space +* Works with envs like `CartPole-v1` + +### Usage + +```bash +poetry install +python cleanrl/ppo.py --help +python cleanrl/ppo.py --env-id CartPole-v1 +``` + +### Implementation details + +[ppo.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py) includes the 11 core implementation details: + +1. Vectorized architecture (:material-github: [common/cmd_util.py#L22](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/common/cmd_util.py#L22)) +1. Orthogonal Initialization of Weights and Constant Initialization of biases (:material-github: [a2c/utils.py#L58)](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/a2c/utils.py#L58)) +1. The Adam Optimizer's Epsilon Parameter (:material-github: [ppo2/model.py#L100](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/model.py#L100)) +1. Adam Learning Rate Annealing (:material-github: [ppo2/ppo2.py#L133-L135](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/ppo2.py#L133-L135)) +1. Generalized Advantage Estimation (:material-github: [ppo2/runner.py#L56-L65](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/runner.py#L56-L65)) +1. Mini-batch Updates (:material-github: [ppo2/ppo2.py#L157-L166](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/ppo2.py#L157-L166)) +1. Normalization of Advantages (:material-github: [ppo2/model.py#L139](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/model.py#L139)) +1. Clipped surrogate objective (:material-github: [ppo2/model.py#L81-L86](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/model.py#L81-L86)) +1. Value Function Loss Clipping (:material-github: [ppo2/model.py#L68-L75](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/model.py#L68-L75)) +1. Overall Loss and Entropy Bonus (:material-github: [ppo2/model.py#L91](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/model.py#L91)) +1. Global Gradient Clipping (:material-github: [ppo2/model.py#L102-L108](https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/model.py#L102-L108)) + + +### Experiment results + +PR :material-github: [vwxyzjn/cleanrl#120](https://github.com/vwxyzjn/cleanrl/pull/120) tracks our effort to conduct experiments, and the reprodudction instructions can be found at :material-github: [vwxyzjn/cleanrl/benchmark/ppo](https://github.com/vwxyzjn/cleanrl/tree/master/benchmark/ppo). + +Below are the average episodic returns for `ppo.py`. To ensure the quality of the implementation, we compared the results against `openai/baselies`' PPO. + +| Environment | `ppo.py` | `openai/baselies`' PPO +| ----------- | ----------- | ----------- | +| CartPole-v1 | 488.75 ± 18.40 |497.54 ± 4.02 | +| Acrobot-v1 | -82.48 ± 5.93 | -81.82 ± 5.58 | +| MountainCar-v0 | -200.00 ± 0.00 | -200.00 ± 0.00 | + + +Learning curves: + +
+ + + + + +
+ + +Tracked experiments and game play videos: + + + +### Video tutorial + +If you'd like to learn `ppo.py` in-depth, consider checking out the following video tutorial: +[![PPO1](ppo/ppo-1-title.png)](https://youtu.be/MEt6rrxH8W4) + + +## `ppo_atari.py` + +The [ppo_atari.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari.py) has the following features: + +* For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques. +* Works with the Atari's pixel `Box` observation space of shape `(210, 160, 3)` +* Works with the `Discerete` action space +* Includes the 9 Atari-specific implementation details as shown in the following video tutorial + [![PPO2](ppo/ppo-2-title.png)](https://youtu.be/05RMTj-2K_Y) + +## `ppo_continuous_action.py` + +The [ppo_continuous_action.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_continuous_action.py) has the following features: + +* For continuous action space. Also implemented Mujoco-specific code-level optimizations +* Works with the `Box` observation space of low-level features +* Works with the `Box` (continuous) action space +* Includes the 8 implementation details for as shown in the following video tutorial (need fixing) + [![PPO3](ppo/ppo-3-title.png)](https://youtu.be/05RMTj-2K_Y) diff --git a/docs/rl-algorithms/ppo/Acrobot-v1.png b/docs/rl-algorithms/ppo/Acrobot-v1.png new file mode 100644 index 0000000000000000000000000000000000000000..f6e9a7a309f2b027973e2c2750215ed180b23e67 GIT binary patch literal 1093212 zcmeEuXHb;ewk@Kdf`AAJ5{-aFm7Gxo3`EIUL2{DN; zoN1a2P42gPZ_mB2-aWUur{1Yo^{U)I+?3VG`sSK*j4{WYAHgb0@?<3RBm@KmWDg(Q ze?mZTKAC`k1FRS2=%>_7BqjLE1l>_K&9hKXc#@Z~o!U zKfL+JJ^0u3!5`H4gBpKO;}2^5L5+WIum9;c|Fz7(Me$F}{>1E0kN$u5Xa5{uj#D3h z^x1I{`mb^Q6SF@t`+ucruXx-Z{>LuB9~Jqp@!p?0{Zps^uj=$qZT_jv|Ed)JbSwWl z0{w~EpP2o3)3-m2{;%i8pP2oL*&h}3N1y$FpwFbu{9=sUZ{*fZq_%HWe9sMZ4PIH! 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rename to docs/rl-algorithms/ppo/ppo-3-title.png diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css index e69de29bb..2180042d6 100644 --- a/docs/stylesheets/extra.css +++ b/docs/stylesheets/extra.css @@ -0,0 +1,6 @@ +.grid-container { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(240px, 1fr)); + image-rendering: -webkit-optimize-contrast; + grid-gap: 50px; +} \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 18a28ee1b..a377815e4 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -41,6 +41,9 @@ markdown_extensions: - pymdownx.superfences - attr_list - md_in_html + - pymdownx.emoji: + emoji_index: !!python/name:materialx.emoji.twemoji + emoji_generator: !!python/name:materialx.emoji.to_svg # - toc: # permalink: true # - markdown.extensions.codehilite: @@ -70,6 +73,7 @@ nav: - cloud/installation.md - cloud/submit-experiments.md - RL Algorithms: + - rl-algorithms/overview.md - rl-algorithms/ppo.md - rl-algorithms/dqn.md - Open RL Benchmark: open-rl-benchmark.md