# Coach **Repository Path**: merryfish/Coach ## Basic Information - **Project Name**: Coach - **Description**: Coach 是 Intel Nervana 开源的一个强化学习研究框架，包含许多最先进的算法的实现 - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 2 - **Created**: 2018-05-27 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Coach [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/NervanaSystems/coach/blob/master/LICENSE) [![Docs](https://img.shields.io/readthedocs/pip/stable.svg)](http://NervanaSystems.github.io/coach/) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1134898.svg)](https://doi.org/10.5281/zenodo.1134898) ## Overview Coach is a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms, and allows simple integration of new environments to solve. Basic RL components (algorithms, environments, neural network architectures, exploration policies, ...) are well decoupled, so that extending and reusing existing components is fairly painless. Training an agent to solve an environment is as easy as running: ```bash python3 coach.py -p CartPole_DQN -r ``` Doom Deathmatch CARLA MontezumaRevenge Blog posts from the Intel® AI website: * [Release 0.8.0](https://ai.intel.com/reinforcement-learning-coach-intel/) (initial release) * [Release 0.9.0](https://ai.intel.com/reinforcement-learning-coach-carla-qr-dqn/) Contacting the Coach development team is also possible through the email [coach@intel.com](coach@intel.com) ## Table of Contents - [Coach](#coach) * [Overview](#overview) * [Documentation](#documentation) * [Installation](#installation) + [Coach Installer](#coach-installer) + [TensorFlow GPU Support](#tensorflow-gpu-support) * [Usage](#usage) + [Running Coach](#running-coach) + [Running Coach Dashboard (Visualization)](#running-coach-dashboard-visualization) + [Parallelizing an Algorithm](#parallelizing-an-algorithm) * [Supported Environments](#supported-environments) * [Supported Algorithms](#supported-algorithms) * [Citation](#citation) * [Disclaimer](#disclaimer) ## Documentation Framework documentation, algorithm description and instructions on how to contribute a new agent/environment can be found [here](http://NervanaSystems.github.io/coach/). ## Installation Note: Coach has only been tested on Ubuntu 16.04 LTS, and with Python 3.5. ### Coach Installer Coach's installer will setup all the basics needed to get the user going with running Coach on top of [OpenAI Gym](https://github.com/openai/gym) environments. This can be done by running the following command and then following the on-screen printed instructions: ```bash ./install.sh ``` Coach creates a virtual environment and installs in it to avoid changes to the user's system. In order to activate and deactivate Coach's virtual environment: ```bash source coach_env/bin/activate ``` ```bash deactivate ``` In addition to OpenAI Gym, several other environments were tested and are supported. Please follow the instructions in the Supported Environments section below in order to install more environments. ### TensorFlow GPU Support Coach's installer installs [Intel-Optimized TensorFlow](https://software.intel.com/en-us/articles/intel-optimized-tensorflow-wheel-now-available), which does not support GPU, by default. In order to have Coach running with GPU, a GPU supported TensorFlow version must be installed. This can be done by overriding the TensorFlow version: ```bash pip3 install tensorflow-gpu ``` ## Usage ### Running Coach Coach supports both TensorFlow and neon deep learning frameworks. Switching between TensorFlow and neon backends is possible by using the `-f` flag. Using TensorFlow (default): `-f tensorflow` Using neon: `-f neon` There are several available presets in presets.py. To list all the available presets use the `-l` flag. To run a preset, use: ```bash python3 coach.py -r -p ``` For example: 1. CartPole environment using Policy Gradients: ```bash python3 coach.py -r -p CartPole_PG ``` 2. Pendulum using Clipped PPO: ```bash python3 coach.py -r -p Pendulum_ClippedPPO -n 8 ``` 3. MountainCar using A3C: ```bash python3 coach.py -r -p MountainCar_A3C -n 8 ``` 4. Doom basic level using Dueling network and Double DQN algorithm: ```bash python3 coach.py -r -p Doom_Basic_Dueling_DDQN ``` 5. Doom health gathering level using Mixed Monte Carlo: ```bash python3 coach.py -r -p Doom_Health_MMC ``` It is easy to create new presets for different levels or environments by following the same pattern as in presets.py More usage examples can be found [here](http://NervanaSystems.github.io/coach/usage/index.html). ### Running Coach Dashboard (Visualization) Training an agent to solve an environment can be tricky, at times. In order to debug the training process, Coach outputs several signals, per trained algorithm, in order to track algorithmic performance. While Coach trains an agent, a csv file containing the relevant training signals will be saved to the 'experiments' directory. Coach's dashboard can then be used to dynamically visualize the training signals, and track algorithmic behavior. To use it, run: ```bash python3 dashboard.py ``` Coach Design ### Parallelizing an Algorithm Since the introduction of [A3C](https://arxiv.org/abs/1602.01783) in 2016, many algorithms were shown to benefit from running multiple instances in parallel, on many CPU cores. So far, these algorithms include [A3C](https://arxiv.org/abs/1602.01783), [DDPG](https://arxiv.org/pdf/1704.03073.pdf), [PPO](https://arxiv.org/pdf/1707.06347.pdf), and [NAF](https://arxiv.org/pdf/1610.00633.pdf), and this is most probably only the begining. Parallelizing an algorithm using Coach is straight-forward. The following method of NetworkWrapper parallelizes an algorithm seamlessly: ```python network.train_and_sync_networks(current_states, targets) ``` Once a parallelized run is started, the ```train_and_sync_networks``` API will apply gradients from each local worker's network to the main global network, allowing for parallel training to take place. Then, it merely requires running Coach with the ``` -n``` flag and with the number of workers to run with. For instance, the following command will set 16 workers to work together to train a MuJoCo Hopper: ```bash python3 coach.py -p Hopper_A3C -n 16 ``` ## Supported Environments * *OpenAI Gym:* Installed by default by Coach's installer. * *ViZDoom:* Follow the instructions described in the ViZDoom repository - https://github.com/mwydmuch/ViZDoom Additionally, Coach assumes that the environment variable VIZDOOM_ROOT points to the ViZDoom installation directory. * *Roboschool:* Follow the instructions described in the roboschool repository - https://github.com/openai/roboschool * *GymExtensions:* Follow the instructions described in the GymExtensions repository - https://github.com/Breakend/gym-extensions Additionally, add the installation directory to the PYTHONPATH environment variable. * *PyBullet:* Follow the instructions described in the [Quick Start Guide](https://docs.google.com/document/d/10sXEhzFRSnvFcl3XxNGhnD4N2SedqwdAvK3dsihxVUA) (basically just - 'pip install pybullet') * *CARLA:* Download release 0.7 from the CARLA repository - https://github.com/carla-simulator/carla/releases Create a new CARLA_ROOT environment variable pointing to CARLA's installation directory. A simple CARLA settings file (```CarlaSettings.ini```) is supplied with Coach, and is located in the ```environments``` directory. ## Supported Algorithms Coach Design * [Deep Q Network (DQN)](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) ([code](agents/dqn_agent.py)) * [Double Deep Q Network (DDQN)](https://arxiv.org/pdf/1509.06461.pdf) ([code](agents/ddqn_agent.py)) * [Dueling Q Network](https://arxiv.org/abs/1511.06581) * [Mixed Monte Carlo (MMC)](https://arxiv.org/abs/1703.01310) ([code](agents/mmc_agent.py)) * [Persistent Advantage Learning (PAL)](https://arxiv.org/abs/1512.04860) ([code](agents/pal_agent.py)) * [Categorical Deep Q Network (C51)](https://arxiv.org/abs/1707.06887) ([code](agents/categorical_dqn_agent.py)) * [Quantile Regression Deep Q Network (QR-DQN)](https://arxiv.org/pdf/1710.10044v1.pdf) ([code](agents/qr_dqn_agent.py)) * [Bootstrapped Deep Q Network](https://arxiv.org/abs/1602.04621) ([code](agents/bootstrapped_dqn_agent.py)) * [N-Step Q Learning](https://arxiv.org/abs/1602.01783) | **Distributed** ([code](agents/n_step_q_agent.py)) * [Neural Episodic Control (NEC)](https://arxiv.org/abs/1703.01988) ([code](agents/nec_agent.py)) * [Normalized Advantage Functions (NAF)](https://arxiv.org/abs/1603.00748.pdf) | **Distributed** ([code](agents/naf_agent.py)) * [Policy Gradients (PG)](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf) | **Distributed** ([code](agents/policy_gradients_agent.py)) * [Asynchronous Advantage Actor-Critic (A3C)](https://arxiv.org/abs/1602.01783) | **Distributed** ([code](agents/actor_critic_agent.py)) * [Deep Deterministic Policy Gradients (DDPG)](https://arxiv.org/abs/1509.02971) | **Distributed** ([code](agents/ddpg_agent.py)) * [Proximal Policy Optimization (PPO)](https://arxiv.org/pdf/1707.06347.pdf) ([code](agents/ppo_agent.py)) * [Clipped Proximal Policy Optimization](https://arxiv.org/pdf/1707.06347.pdf) | **Distributed** ([code](agents/clipped_ppo_agent.py)) * [Direct Future Prediction (DFP)](https://arxiv.org/abs/1611.01779) | **Distributed** ([code](agents/dfp_agent.py)) * Behavioral Cloning (BC) ([code](agents/bc_agent.py)) ## Citation If you used Coach for your work, please use the following citation: ``` @misc{caspi_itai_2017_1134899, author = {Caspi, Itai and Leibovich, Gal and Novik, Gal}, title = {Reinforcement Learning Coach}, month = dec, year = 2017, doi = {10.5281/zenodo.1134899}, url = {https://doi.org/10.5281/zenodo.1134899} } ``` ## Disclaimer Coach is released as a reference code for research purposes. It is not an official Intel product, and the level of quality and support may not be as expected from an official product. Additional algorithms and environments are planned to be added to the framework. Feedback and contributions from the open source and RL research communities are more than welcome.