Overview:

RLcycle is a reinforcement learning (RL) agents framework that provides ready-made RL agents and reusable components for easy prototyping. It offers a range of RL algorithms, including DQN, Distributional, A2C, A3C, DDPG, Soft Actor Critic, and more. RLcycle utilizes PyTorch for computations and building models, Hydra for configurations and building agents, Ray for parallelizing learning, and WandB for logging training and testing. The framework aims to simplify the process of building and running RL agents.

Features:

Ready-made RL agents: RLcycle provides a variety of RL agents, including DQN with enhancements such as Distributional, Quantile Regression, Rainbow-DQN, as well as Noisy Networks for parameter space noise.
A2C and A3C: RLcycle supports both data parallel A2C and gradient parallel A3C algorithms.
DDPG: RLcycle offers both Lillicrap et al. (2015) and Fujimoto et al. (2018) versions of the DDPG algorithm.
Soft Actor Critic: RLcycle includes Soft Actor Critic with automatic entropy coefficient tuning.
Prioritized Experience Replay and n-step updates: RLcycle provides support for prioritized experience replay and n-step updates for all off-policy algorithms.

Summary:

RLcycle is a reinforcement learning (RL) agents framework that aims to simplify the process of building and running RL agents. It offers a range of ready-made RL agents and reusable components for easy prototyping. The framework utilizes PyTorch, Hydra, Ray, and WandB for computations, configurations, parallelizing learning, and logging respectively. With support for various RL algorithms and features such as prioritized experience replay and n-step updates, RLcycle provides a comprehensive solution for RL practitioners. The framework also has plans for future updates, including the incorporation of TRPO, PPO, and compatibility with a distributed RL framework. Overall, RLcycle provides a valuable resource for researchers and developers in the field of reinforcement learning.

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RLcycle