Skip to content

PyTorch Implementation of Ape-X (Distributed prioritized experience replay) architecture with DQN learner

Notifications You must be signed in to change notification settings

praveen-palanisamy/Ape-X-DQN

Repository files navigation

PyTorch Implementation of Ape-X (Distributed prioritized experience replay) architecture with DQN learner

  • Easy-to-follow implementation with comments indicating the algorithm line as described in the paper

Running the code

  1. Setup a conda env with the necessary python packages. Assuming Anaconda is installed, you can run the following command from the root of this repository: conda env create -f conda_env.yaml -n "apex_dqn_pytorch"

  2. Set the configuration parameters suitable for your hardware in parameters.json At the minimum, you should set the num_actors parameter under "Actor" and the "Replay Memory" 'soft_capacity` based on the number of CPU cores and RAM available.

  3. Launch the training process using the following command: python main.py

You should see episode stats printed out to the console. You can change the learning environment using the "name"parameter value under "env_conf" in parameters.json

To-Dos:
  • Compress state/observations using PNG codec before storing in memory and decompress when needed
  • Bias correction in prioritized replay using importance sampling

About

PyTorch Implementation of Ape-X (Distributed prioritized experience replay) architecture with DQN learner

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages