dexterfixxor/distributedrl

A framework for easy prototyping of distributed reinforcement learning algorithms

distributedRL

distributedRL is a framework for prototyping disrtibuted reinforcement learning using Pytorch, Ray, and ZeroMQ (and more). You can make use of the structural components to easily distribute your reinforcement learning algorithm, on a single-machine.

Currently, an implementation of Ape-X DQN is available. IMPALA and SEED are planned to be implemented.

Feel free to reach out (cjy2129 at columbia dot edu) or raise an issue if you have any questions!

A note about a few choices

• ApeXLearner and ApeXWorker are implemented as abstract classes (that inherit common.abstract.Learner and common.abstract.Worker respectively). To extend Ape-X to any off-policy RL algorithm, you just have to implement algorithm-specific details, such as action selection, environment step, and learning step (check the abstract classes for more detail).
• I use ZeroMQ for inter-process communication instead of the Ray's built-in features. (1) I wanted the data-passing mechanism and serialization to be a bit more explicit, and (2) ray's inter-process communication forbids calling remote operations, which I found to be a bit restricting for this purpose.

Installation

clone the repository, then

conda env create -f environment.yml
conda activate distrl
pip install -e .

A Short Tutorial

To be added

Benchmarking

To be added

Acknowledgements

I thank @Avrech for his helpful discussion, suggestions and enhancements. In particular, on identifying bugs and bottlenecks and improving asynchrony of the Ape-X implementation.

References/Papers:

• Distributed Prioritized Experience Replay (Horgan et al., 2018), ICLR 2018