TF2RL

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

1. 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
CURL, SAC-AE	-	✓	-	Model-free Off-policy RL
MPC, ME-TRPO	✓	✓	-	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
  • Improving Sample Efficiency in Model-Free Reinforcement Learning from Images, code
  • CURL: Contrastive Unsupervised Representations for Reinforcement Learning, code
• Model-base RL
  • Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning, code
  • Model-Ensemble Trust-Region Policy Optimization, 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
• D2RL, code

2. Installation

There are several ways to install tf2rl. The recommended way is "2.1 Install from PyPI".

If TensorFlow is already installed, we try to identify the best version of TensorFlow Probability.

2.1 Install from PyPI

You can install tf2rl from PyPI:

$ pip install tf2rl

2.2 Install from Source Code

You can also install from source:

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

2.3 Preinstalled Docker Container

Instead of installing tf2rl on your (virtual) system, you can use preinstalled Docker containers.

Only the first execution requires time to download the container image.

At the following commands, you need to replace <version> with the version tag which you want to use.

2.3.1 CPU Only

The following simple command starts preinstalled container.

$ docker run -it ghcr.io/keiohta/tf2rl/cpu:<version> bash

If you also want to mount your local directory /local/dir/path at container /mount/point

$ docker run -it -v /local/dir/path:/mount/point ghcr.io/keiohta/tf2rl/cpu:<version> bash

2.3.2 GPU Support (Linux Only, Experimental)

WARNING: We encountered unsolved errors when running ApeX multiprocess learning.

Requirements

• Linux
• NVIDIA GPU
  • TF2.2 compatible driver
• Docker 19.03 or later

The following simple command starts preinstalled container.

$ docker run --gpus all -it ghcr.io/keiohta/tf2rl/nvidia:<version> bash

If you also want to mount your local directory /local/dir/path at container /mount/point

$ docker run --gpus all -it -v /local/dir/path:/mount/point ghcr.io/keiohta/tf2rl/nvidia:<version> bash

If your container can see GPU correctly, you can check inside container by the following comand;

$ nvidia-smi

3. 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-v1")
test_env = gym.make("Pendulum-v1")
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

4. Usage

In basic usage, what you need is initializing one of the policy classes and Trainer class.

As a option, tf2rl supports command line program style, so that you can also pass configuration parameters from command line arguments.

4.1 Command Line Program Style

Trainer class and policy classes have class method get_argument, which creates or updates ArgParser object.

You can parse the command line arguments with the ArgParser.parse_args method, which returns Namespace object.

Policy's constructor option can be extracted from the Namespace object explicitly. Trainer constructor accepts the Namespace object.

from tf2rl.algos.dqn import DQN
from tf2rl.experiments.trainer import Trainer

env = ... # Create gym.env like environment.

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

policy = DQN(enable_double_dqn = args.enable_double_dqn,
             enable_dueling_dqn = args.enable_dueling_dqn,
			 enable_noisy_dqn = args.enable_noisy_dqn)
trainer = Trainer(policy, env, args)
trainer()

4.2 Non Command Line Program Style (e.g. on Jupyter Notebook)

ArgParser doesn't fit the usage on Jupyter Notebook like envrionment. Trainer constructor can accept dict as args argument instead of Namespace object.

from tf2rl.algos.dqn import DQN
from tf2rl.experiments.trainer import Trainer

env = ... # Create gym.env like environment.

policy = DQN( ... )
trainer = Trainer(policy, env, {"max_steps": int(1e+6), ... })
trainer()

4.3 Results

The Trainer class saves logs and models under <logdir>/%Y%m%dT%H%M%S.%f. The default logdir is "results", and it can be changed by --logdir command argument or "logdir" key in constructor args.

5. Citation

@misc{ota2020tf2rl,
  author = {Kei Ota},
  title = {TF2RL},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/keiohta/tf2rl/}}
}