Papers › Policy Distillation

Policy Distillation

19 Nov 2015arXiv:1511.06295archive 2025-07-28

Andrei A. Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, Raia Hadsell

Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specific) networks and extensive training are needed to achieve good performance. In this work, we present a novel method called policy distillation that can be used to extract the policy of a reinforcement learning agent and train a new network that performs at the expert level while being dramatically smaller and more efficient. Furthermore, the same method can be used to consolidate multiple task-specific policies into a single policy. We demonstrate these claims using the Atari domain and show that the multi-task distilled agent outperforms the single-task teachers as well as a jointly-trained DQN agent.

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dsapandora/s_cera mentioned on GitHubtfGPL-3.0 report

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Tasks

Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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Convolution DQN Dense Connections Q-Learning

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