Rlgames

This repository demonstrates practical proficiency in implementing classic and modern Reinforcement Learning algorithms using PyTorch. The code structure is highly domain-specific and follows best practices for RL project organization. * The architecture cleanly isolates core RL components—Agent, Model, and Trainer—demonstrating strong modularity for algorithm expansion and testing. * Successfully explores advanced RL techniques, notably A3C leveraging specialized `SharedAdam` for robust multi-process parallel optimization strategies. * Review the DQN update mechanism; introduce a separate target network to stabilize the crucial Bellman iteration and prevent catastrophic forgetting. * Feature representation techniques, like the robust `TileCoder` for function approximation, are effectively employed on continuous `gymnasium` environments.