unstable-baselines 0.1.0a0

A TensorFlow 2.0 implemented RL baselines

Unstable Baselines (Early Access)

A Deep Reinforcement Learning codebase in TensorFlow 2.0 with an unified, flexible and highly customizable structure for fast prototyping.

Features	Unstable Baselines	Stable-Baselines3	OpenAI Baselines
State of the art RL methods	:heavy_minus_sign: (1)	:heavy_check_mark:	:heavy_check_mark:
Documentation	:x:	:heavy_check_mark:	:x:
Custom callback (2)	:x:	:vomiting_face:	:heavy_minus_sign:
TensorFlow 2.0 support	:heavy_check_mark:	:x:	:x:
Clean, elegant code	:heavy_check_mark:	:x:	:x:
Easy to trace, customize	:heavy_check_mark:	:x: (3)	:x: (3)
Standalone implementations	:heavy_check_mark:	:heavy_minus_sign:	:x: (4)

^{(1) Currently only support DQN, C51, PPO, TD3, ...etc. We are still working on other algorithms.
(2) For example, in Stable-Baselines, you need to write this disgusting custom callback to save the best-performed model :vomiting_face:, while in Unstable Baselines, they are automatically saved.
(3) If you have traced Stable-baselines or OpenAI/baselines once, you'll never do that again.
(4) Many cross-dependencies across all algos make the code very hard to trace, for example baselines/common/policies.py, baselines/a2c/a2c.py.... Great job! OpenAI!:cat:}

Documentation

We don't have any documentation yet.

Installation

Basic requirements:

• Python >= 3.6
• TensorFlow (CPU/GPU) >= 2.3.0

You can install from PyPI

$ pip install unstable_baselines

Or you can also install the latest version from this repository

$ pip install git+https://github.com/Ending2015a/unstable_baselines.git@master

Done! Now, you can

• Go through the Quick Start section
• Or run the example codes in example folder.

Algorithms

Model-free RL

Algorithm	Box	Discrete	MultiDiscrete	MultiBinary
DQN	:x:	:heavy_check_mark:	:x:	:x:
PPO	:heavy_check_mark:	:heavy_check_mark:	:x:	:x:
TD3	:heavy_check_mark:	:x:	:x:	:x:
SD3	:heavy_check_mark:	:x:	:x:	:x:

• 2021.09.17: DQN supports
  • Multi-step learning
  • Prioritized experience replay: arXiv:1511.05952
  • Dueling network: arXiv:1511.06581
• 2021.04.19: Implemented DQN
  • From paper: arXiv:1509.06461
• 2021.03.27: PPO support continuous (Box) action space
• 2021.03.23: Implemented SD3
  • From paper: arXiv:2010.09177
• 2021.03.20: Implemented TD3
  • From paper: arXiv:1802.09477
• 2021.03.10: Implemented PPO
  • From paper: arXiv:1707.06347

Distributional RL

Algorithm	Box	Discrete	MultiDiscrete	MultiBinary
C51	:x:	:heavy_check_mark:	:x:	:x:
QRDQN	:x:	:heavy_check_mark:	:x:	:x:
IQN	:x:	:heavy_check_mark:	:x:	:x:

• 2021.04.28: Implemented IQN
  • From paper: arXiv:1806.06923
• 2021.04.21: Implemented QRDQN
  • From paper: arXiv:1710.10044
• 2021.04.20: Implemented C51
  • From paper: arXiv:1707.06887

Quick Start

This example shows how to train a PPO agent to play CartPole-v0. You can find the full scripts in example/cartpole/train_ppo.py.

First, import dependencies

import gym
import unstable_baselines as ub
from unstable_baselines.algo.ppo import PPO

Create environments for training and evaluation

# create environments
env = ub.envs.VecEnv([gym.make('CartPole-v0') for _ in range(10)])
eval_env = gym.make('CartPole-v0')

Create a PPO model and train it

model = PPO(
    env,
    learning_rate=1e-3,
    gamma=0.8,
    batch_size=128,
    n_steps=500
).learn(  # train for 20000 steps
    20000,
    verbose=1
)

Save and load the trained model

model.save('./my_ppo_model')
model = PPO.load('./my_ppo_model')

Evaluate the training results

model.eval(eval_env, 20, 200, render=True)
# don't forget to close the environments!
env.close()
eval_env.close()

More examples:

• Playing with Atari
• Solving continuous control problems

Update Logs

• 2021.05.22: Add benchmarks
• 2021.04.27: Update to framework v2: supports saving/loading the best performed checkpoints.