DeepRL
This project features a modular implementation of deep reinforcement learning algorithms using PyTorch. It seamlessly transitions from simple tasks to complex games, incorporating methods like Double DQN, A2C, and PPO. With efficient data generation and hardware optimization, it's suitable for scalable deep learning research. Support is available for robust testing environments such as Breakout and Mujoco. Discover innovative algorithmic insights and performance metrics visualized through detailed learning curves.
