Deep Attention Recurrent Q-Network

A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels. Its creators at the Google DeepMind's team called the approach: Deep Q-Network (DQN). We present an extension of DQN by "soft" and "hard" attention mechanisms. Tests of the proposed Deep Attention Recurrent Q-Network (DARQN) algorithm on multiple Atari 2600 games show level of performance superior to that of DQN. Moreover, built-in attention mechanisms allow a direct online monitoring of the training process by highlighting the regions of the game screen the agent is focusing on when making decisions.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Atari Games Atari 2600 Breakout DARQN hard Score 20 # 50
Atari Games Atari 2600 Gopher DARQN soft Score 5356 # 35
Atari Games Atari 2600 Seaquest DARQN soft Score 7263 # 27
Atari Games Atari 2600 Space Invaders DARQN soft Score 650 # 46
Atari Games Atari 2600 Tutankham DARQN soft Score 197 # 24

Methods