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


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:

Also, some useful techniques are implemented:


You can install tf2rl from PyPI:

$ pip install tf2rl

or, you can also install from source:

$ git clone 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(
    gpu=-1,  # Run on CPU. If you want to run on GPU, specify GPU number
trainer = Trainer(policy, env, args, test_env=test_env)

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 [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