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A simple framework for distributed reinforcement learning in PyTorch.

Project description

rltorch(WIP)

rltorch provides a simple framework for reinforcement learning in PyTorch. You can easily implement distributed RL algorithms.

Installation

Install rltorch from source.

git clone https://github.com/ku2482/rltorch.git
cd rltorch
pip install -e .

You can also install using PyPI.

pip install rltorch

Examples

Ape-X

You can implement Ape-X[1] agent like this example here.

python examples/atari/apex.py \
[--env_id str(default MsPacmanNoFrameskip-v4)] \
[--num_actors int(default 4)] [--cuda (optional)] \
[--seed int(default 0)]

Soft Actor-Critic

You can implement Soft Actor-Critic[2, 3] agent like this example here. Note that you need a license and mujoco_py to be installed.

python examples/mujoco/sac.py \
[--env_id str(default HalfCheetah-v2)] \
[--num_actors int(default 1)] \
[--cuda (optional)] [--seed int(default 0)]

SAC-Discrete

You can implement SAC-Discrete[4] agent like this example here.

python examples/mujoco/sac.py \
[--env_id str(default MsPacmanNoFrameskip-v4)] \
[--num_actors int(default 4)] \
[--cuda (optional)] [--seed int(default 0)]

References

[1] Horgan, Dan, et al. "Distributed prioritized experience replay." arXiv preprint arXiv:1803.00933 (2018).

[2] Haarnoja, Tuomas, et al. "Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor." arXiv preprint arXiv:1801.01290 (2018).

[3] Haarnoja, Tuomas, et al. "Soft actor-critic algorithms and applications." arXiv preprint arXiv:1812.05905 (2018).

[4] Christodoulou, Petros. "Soft Actor-Critic for Discrete Action Settings." arXiv preprint arXiv:1910.07207 (2019).

Project details


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