On Improving Deep Reinforcement Learning for POMDPs

Understand

Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go.

• However, very little work has been done in deep RL to handle partially observable environments.
• We propose a new architecture called Action-specific Deep Recurrent Q-Network (ADRQN) to enhance learning performance in partially observable domains.
• Actions are encoded by a fully connected layer and coupled with a convolutional observation to form an action-observation pair.