An Implementation in Pytorch.

Introduction

Over the last few articles, we’ve discussed and implemented Deep Q-learning (DQN)and Double Deep Q Learning (DDQN) in the VizDoom game environment and evaluated their performance. Deep Q-learning is a highly flexible and responsive online learning approach that utilizes rapid intra-episodic updates to it’s estimations of state-action (Q) values in an environment in order to maximize reward. Double Deep Q-Learning builds upon this by decoupling the networks responsible for action selection and TD-target calculation in order to minimize Q-value overestimation, a problem particularly evident when earlier on in the training process, when the agent has yet to fully explore the majority of possible states.

#doom #deep-learning #ai #openai #reinforcement-learning