Max van Dijck 232c331ce3 [RLlib] Rename all np.product usage to np.prod (#46317) 3 月之前
..
examples 67593a914a [RLlib Contrib] APEX DQN. (#36591) 1 年之前
src 67593a914a [RLlib Contrib] APEX DQN. (#36591) 1 年之前
tests 67593a914a [RLlib Contrib] APEX DQN. (#36591) 1 年之前
tuned_examples a9ac55d4f2 [RLlib; RLlib contrib] Move `tuned_examples` into rllib_contrib and remove CI learning tests for contrib algos. (#40444) 1 年之前
BUILD a9ac55d4f2 [RLlib; RLlib contrib] Move `tuned_examples` into rllib_contrib and remove CI learning tests for contrib algos. (#40444) 1 年之前
README.md 67593a914a [RLlib Contrib] APEX DQN. (#36591) 1 年之前
pyproject.toml 232c331ce3 [RLlib] Rename all np.product usage to np.prod (#46317) 3 月之前
requirements.txt 232c331ce3 [RLlib] Rename all np.product usage to np.prod (#46317) 3 月之前

README.md

APEX DQN (Distributed Prioritized Experience Replay)

APEX DQN Distributed Prioritized Experience Replay is an algorithm that decouples active learning from sampling. Actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared experience replay memory; the learner replays samples of experience and updates the neural network. The architecture relies on prioritized experience replay to focus only on the most significant data generated by the actors.

Installation

conda create -n rllib-apex-dqn python=3.10
conda activate rllib-apex-dqn
pip install -r requirements.txt
pip install -e '.[development]'

Usage

[APEX-DQN Example]()