2017/04/26 by Pengfei Zhu, Xin Li, Zhu, Pengfei +5 · 5 citations
Computer Science · Engineering · #Elevator Systems and Control #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and ELM #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.1704.07978
openalex publication_date 2017/04/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. The time series of action-observation pairs are then integrated by an LSTM layer that learns latent states based on which a fully connected layer computes Q-values as in conventional Deep Q-Networks (DQNs). We demonstrate the effectiveness of our new architecture in several partially observable domains, including flickering Atari games.