dqn

A concise introduction to Reinforcement Learning, from basics to applied algorithms, with PyTorch implementations and supplementary proofs.

This paper presents a cross-sectional study comparing various action factorization methods (independent networks, shared encoder, VDN, QPLEX, Joint, Auto-Regressive) across three RL algorithm families (PPO, SAC, DQN) in hybrid discrete-continuous action spaces, introducing two new lightweight environments and variants VDN-PPO and PPO-MIX.

OpenAI shares lessons learned while implementing DQN as part of their Baselines project, covering debugging tips such as greyscale calibration issues, hyperparameter tuning, and correct interpretation of the Huber Loss in the original Nature paper.