eswarraop/tf2rl

TensorFlow2 Reinforcement Learning

TF2RL

TF2RL is a deep reinforcement learning library that implements various deep reinforcement learning algorithms using TensorFlow 2.x.

Algorithms

Following algorithms are supported:

Algorithm	Dicrete action	Continuous action	Support	Category
VPG, PPO	✓	✓	GAE	Model-free On-policy RL
DQN (including DDQN, Prior. DQN, Duel. DQN, Distrib. DQN, Noisy DQN)	✓	-	ApeX	Model-free Off-policy RL
DDPG (including TD3, BiResDDPG)	-	✓	ApeX	Model-free Off-policy RL
SAC	✓	✓	ApeX	Model-free Off-policy RL
MPC	✓	✓	-	Model-base RL
GAIL, GAIfO, VAIL (including Spectral Normalization)	✓	✓	-	Imitation Learning

Following papers have been implemented in tf2rl:

• Model-free On-policy RL
  • Policy Gradient Methods for Reinforcement Learning with Function Approximation, code
  • High-Dimensional Continuous Control Using Generalized Advantage Estimation, code
  • Proximal Policy Optimization Algorithms, code
• Model-free Off-policy RL
  • Playing Atari with Deep Reinforcement Learning, code
  • Human-level control through Deep Reinforcement Learning, code
  • Deep Reinforcement Learning with Double Q-learning, code
  • Prioritized Experience Replay, code
  • Dueling Network Architectures for Deep Reinforcement Learning, code
  • A Distributional Perspective on Reinforcement Learning, code
  • Noisy Networks for Exploration, code
  • Distributed Prioritized Experience Replay, code
  • Continuous control with deep reinforcement learning, code
  • Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, Soft Actor-Critic Algorithms and Applications, code
  • Addressing Function Approximation Error in Actor-Critic Methods, code
  • Deep Residual Reinforcement Learning, code
  • Soft Actor-Critic for Discrete Action Settings, code
• Model-base RL
  • Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning, code
• Imitation Learning
  • Generative Adversarial Imitation Learning, code
  • Spectral Normalization for Generative Adversarial Networks, code
  • Generative Adversarial Imitation from Observation, code
  • Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow, code

Also, some useful techniques are implemented:

• Auto-Encoding Variational Bayes, code

Installation

You can install tf2rl from PyPI:

$ pip install tf2rl

or, you can also install from source:

$ git clone https://github.com/keiohta/tf2rl.git tf2rl
$ cd tf2rl
$ pip install .

Getting started

Here is a quick example of how to train DDPG agent on a Pendulum environment:

import gym
from tf2rl.algos.ddpg import DDPG
from tf2rl.experiments.trainer import Trainer


parser = Trainer.get_argument()
parser = DDPG.get_argument(parser)
args = parser.parse_args()

env = gym.make("Pendulum-v0")
test_env = gym.make("Pendulum-v0")
policy = DDPG(
    state_shape=env.observation_space.shape,
    action_dim=env.action_space.high.size,
    gpu=-1,  # Run on CPU. If you want to run on GPU, specify GPU number
    memory_capacity=10000,
    max_action=env.action_space.high[0],
    batch_size=32,
    n_warmup=500)
trainer = Trainer(policy, env, args, test_env=test_env)
trainer()

You can check implemented algorithms in examples. For example if you want to train DDPG agent:

# You must change directory to avoid importing local files
$ cd examples
# For options, please specify --help or read code for options
$ python run_ddpg.py [options]

You can see the training progress/results from TensorBoard as follows:

# When executing `run_**.py`, its logs are automatically generated under `./results`
$ tensorboard --logdir results