RLs: Reinforcement Learning Algorithm Based On PyTorch.

RLs

This project includes SOTA or classic reinforcement learning (single and multi-agent) algorithms used for training agents by interacting with Unity through ml-agents Release 18 or with gym.

About

The goal of this framework is to provide stable implementations of standard RL algorithms and simultaneously enable fast prototyping of new methods. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research).

Characteristics

This project supports:

Suitable for Windows, Linux, and OSX
Single- and Multi-Agent training.
Multiple type of observation sensors as input.
Only need 3 steps to implement a new algorithm:
1. policy write .py in rls/algorithms/{single/multi} directory and make the policy inherit from super-class defined in rls/algorithms/base
2. config write .yaml in rls/configs/algorithms/ directory and specify the super config type defined in rls/configs/algorithms/general.yaml
3. register register new algorithm in rls/algorithms/__init__.py
Only need 3 steps to adapt to a new training environment:
1. wrapper write environment wrappers in rls/envs/{new platform} directory and make it inherit from super-class defined in rls/envs/env_base.py
2. config write default configuration in rls/configs/{new platform}
3. register register new environment platform in rls/envs/__init__.py
Compatible with several environment platforms
- Unity3D ml-agents.
- PettingZoo
- gym, for now only two data types are compatible——[Box, Discrete]. Support parallel training using gym envs, just need to specify --copies to how many agents you want to train in parallel.
  - environments:
    - MuJoCo(v2.0.2.13)
    - PyBullet
    - gym_minigrid
  - observation -> action:
    - Discrete -> Discrete (observation type -> action type)
    - Discrete -> Box
    - Box -> Discrete
    - Box -> Box
    - Box/Discrete -> Tuple(Discrete, Discrete, Discrete)
Four types of Replay Buffer, Default is ER:
- ER
- Prioritized ER
Noisy Net for better exploration.
Intrinsic Curiosity Module for almost all off-policy algorithms implemented.
Parallel training multiple scenes for Gym
Unified data format

Installation

method 1:

$ git clone https://github.com/StepNeverStop/RLs.git
$ cd RLs
$ conda create -n rls python=3.8
$ conda activate rls
# Windows
$ pip install -e .[windows]
# Linux or Mac OS
$ pip install -e .

method 1:

conda env create -f environment.yaml

If using ml-agents:

$ pip install -e .[unity]

You can download the builded docker image from here:

$ docker pull keavnn/rls:latest

If anyone who wants to send a PR, plz format all code-files first:

$ pip install -e .[pr]
$ python auto_format.py -d ./

Implemented Algorithms

For now, these algorithms are available:

Multi-Agent training algorithms:
Single-Agent training algorithms(Some algorithms that only support continuous space problems use Gumbel-softmax trick to implement discrete versions, i.e. DDPG):
- Policy Gradient, PG
- Actor Critic, AC
- Synchronous Advantage Actor Critic, A2C
- 💥Proximal Policy Optimization, PPO , DPPO
- Trust Region Policy Optimization, TRPO
- Natural Policy Gradient, NPG
- Deterministic Policy Gradient, DPG
- Deep Deterministic Policy Gradient, DDPG
- 🔥Soft Actor Critic, SAC, Discrete SAC
- Tsallis Actor Critic, TAC
- 🔥Twin Delayed Deep Deterministic Policy Gradient, TD3
- Deep Q-learning Network, DQN, 2013 , 2015
- Double Deep Q-learning Network, DDQN
- Dueling Double Deep Q-learning Network, DDDQN
- Deep Recurrent Q-learning Network, DRQN
- Deep Recurrent Double Q-learning, DRDQN
- Category 51, C51
- Quantile Regression DQN, QR-DQN
- Implicit Quantile Networks, IQN
- Rainbow DQN
- MaxSQN
- Soft Q-Learning, SQL
- Bootstrapped DQN
- Averaged DQN
- Hierachical training algorithms:
  - Option-Critic, OC
  - Asynchronous Advantage Option-Critic, A2OC
  - PPO Option-Critic, PPOC
  - Interest-Option-Critic, IOC
- Model-based algorithms:
  - Learning Latent Dynamics for Planning from Pixels, PlaNet
  - Dream to Control, Dreamer
  - Mastering Atari with Discrete World Models, DreamerV2
  - Model-Based Value Estimation, MVE
- Offline algorithms(under implementation):
  - Conservative Q-Learning for Offline Reinforcement Learning, CQL
  - BCQ
    - Benchmarking Batch Deep Reinforcement Learning Algorithms, Discrete
    - Off-Policy Deep Reinforcement Learning without Exploration, Continuous

Algorithms	Discrete	Continuous	Image	RNN	Command parameter
PG	✓	✓	✓	✓	pg
AC	✓	✓	✓	✓	ac
A2C	✓	✓	✓	✓	a2c
NPG	✓	✓	✓	✓	npg
TRPO	✓	✓	✓	✓	trpo
PPO	✓	✓	✓	✓	ppo
DQN	✓		✓	✓	dqn
Double DQN	✓		✓	✓	ddqn
Dueling Double DQN	✓		✓	✓	dddqn
Averaged DQN	✓		✓	✓	averaged_dqn
Bootstrapped DQN	✓		✓	✓	bootstrappeddqn
Soft Q-Learning	✓		✓	✓	sql
C51	✓		✓	✓	c51
QR-DQN	✓		✓	✓	qrdqn
IQN	✓		✓	✓	iqn
Rainbow	✓		✓	✓	rainbow
DPG	✓	✓	✓	✓	dpg
DDPG	✓	✓	✓	✓	ddpg
TD3	✓	✓	✓	✓	td3
SAC(has V network)	✓	✓	✓	✓	sac_v
SAC	✓	✓	✓	✓	sac
TAC	sac	✓	✓	✓	tac
MaxSQN	✓		✓	✓	maxsqn
OC	✓	✓	✓	✓	oc
AOC	✓	✓	✓	✓	aoc
PPOC	✓	✓	✓	✓	ppoc
IOC	✓	✓	✓	✓	ioc
PlaNet	✓		✓	1	planet
Dreamer	✓	✓	✓	1	dreamer
DreamerV2	✓	✓	✓	1	dreamerv2
VDN	✓		✓	✓	vdn
QMIX	✓		✓	✓	qmix
Qatten	✓		✓	✓	qatten
QPLEX	✓		✓	✓	qplex
QTRAN	✓		✓	✓	qtran
MADDPG	✓	✓	✓	✓	maddpg
MASAC	✓	✓	✓	✓	masac
CQL	✓		✓	✓	cql_dqn
BCQ	✓	✓	✓	✓	bcq
MVE	✓	✓			mve

1 means must use rnn or rnn is used by default.

Getting started

"""
usage: run.py [-h] [-c COPIES] [--seed SEED] [-r]
              [-p {gym,unity,pettingzoo}]
              [-a {maddpg,masac,vdn,qmix,qatten,qtran,qplex,aoc,ppoc,oc,ioc,planet,dreamer,dreamerv2,mve,cql_dqn,bcq,pg,npg,trpo,ppo,a2c,ac,dpg,ddpg,td3,sac_v,sac,tac,dqn,ddqn,dddqn,averaged_dqn,c51,qrdqn,rainbow,iqn,maxsqn,sql,bootstrappeddqn}]
              [-i] [-l LOAD_PATH] [-m MODELS] [-n NAME]
              [--config-file CONFIG_FILE] [--store-dir STORE_DIR]
              [--episode-length EPISODE_LENGTH] [--hostname] [-e ENV_NAME]
              [-f FILE_NAME] [-s] [-d DEVICE] [-t MAX_TRAIN_STEP]

optional arguments:
  -h, --help            show this help message and exit
  -c COPIES, --copies COPIES
                        nums of environment copies that collect data in
                        parallel
  --seed SEED           specify the random seed of module random, numpy and
                        pytorch
  -r, --render          whether render game interface
  -p {gym,unity,pettingzoo}, --platform {gym,unity,pettingzoo}
                        specify the platform of training environment
  -a {maddpg,masac,vdn,qmix,qatten,qtran,qplex,aoc,ppoc,oc,ioc,planet,dreamer,dreamerv2,mve,cql_dqn,bcq,pg,npg,trpo,ppo,a2c,ac,dpg,ddpg,td3,sac_v,sac,tac,dqn,ddqn,dddqn,averaged_dqn,c51,qrdqn,rainbow,iqn,maxsqn,sql,bootstrappeddqn}, --algorithm {maddpg,masac,vdn,qmix,qatten,qtran,qplex,aoc,ppoc,oc,ioc,planet,dreamer,dreamerv2,mve,cql_dqn,bcq,pg,npg,trpo,ppo,a2c,ac,dpg,ddpg,td3,sac_v,sac,tac,dqn,ddqn,dddqn,averaged_dqn,c51,qrdqn,rainbow,iqn,maxsqn,sql,bootstrappeddqn}
                        specify the training algorithm
  -i, --inference       inference the trained model, not train policies
  -l LOAD_PATH, --load-path LOAD_PATH
                        specify the name of pre-trained model that need to
                        load
  -m MODELS, --models MODELS
                        specify the number of trails that using different
                        random seeds
  -n NAME, --name NAME  specify the name of this training task
  --config-file CONFIG_FILE
                        specify the path of training configuration file
  --store-dir STORE_DIR
                        specify the directory that store model, log and
                        others
  --episode-length EPISODE_LENGTH
                        specify the maximum step per episode
  --hostname            whether concatenate hostname with the training name
  -e ENV_NAME, --env-name ENV_NAME
                        specify the environment name
  -f FILE_NAME, --file-name FILE_NAME
                        specify the path of builded training environment of
                        UNITY3D
  -s, --save            specify whether save models/logs/summaries while
                        training or not
  -d DEVICE, --device DEVICE
                        specify the device that operate Torch.Tensor
  -t MAX_TRAIN_STEP, --max-train-step MAX_TRAIN_STEP
                        specify the maximum training steps
"""

Example:

python run.py -s    # save model and log while train
python run.py -p gym -a dqn -e CartPole-v0 -c 12 -n dqn_cartpole
python run.py -p unity -a ppo -n run_with_unity -c 1

The main training loop of pseudo-code in this repo is as:

# noinspection PyUnresolvedReferences
agent.episode_reset()  # initialize rnn hidden state or something else
# noinspection PyUnresolvedReferences
obs = env.reset()
while True:
    # noinspection PyUnresolvedReferences
    env_rets = env.step(agent(obs))
    # noinspection PyUnresolvedReferences
    agent.episode_step(obs, env_rets)  # store experience, save model, and train off-policy algorithms
    obs = env_rets['obs']
    if env_rets['done']:
        break
# noinspection PyUnresolvedReferences
agent.episode_end()  # train on-policy algorithms

Giving credit

If using this repository for your research, please cite:

@misc{RLs,
  author = {Keavnn},
  title = {RLs: A Featureless Reinforcement Learning Repository},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/StepNeverStop/RLs}},
}

Issues

Any questions/errors about this project, please let me know in here.

StepNeverStop/RLs

StepNeverStop

Reviews

Repository Details

RLs