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LOOK WHERE YOU LOOK! SALIENCY-GUIDED Q-NETWORKS FOR VISUAL RL TASKS

Abstract : Deep reinforcement learning policies, despite their outstanding efficiency in simulated visual control tasks, have shown disappointing ability to generalize across disturbances in the input training images. Changes in image statistics or distracting background elements are pitfalls that prevent generalization and real-world applicability of such control policies. We elaborate on the intuition that a good visual policy should be able to identify which pixels are important for its decision, and preserve this identification of important sources of information across images. This implies that training of a policy with small generalization gap should focus on such important pixels and ignore the others. This leads to the introduction of saliency-guided Q-networks (SGQN), a generic method for visual reinforcement learning, that is compatible with any value function learning method. SGQN vastly improves the generalization capability of Soft Actor-Critic agents and outperforms existing stateof-the-art methods on the Deepmind Control Generalization benchmark, setting a new reference in terms of training efficiency, generalization gap, and policy interpretability.
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https://hal.archives-ouvertes.fr/hal-03777742
Contributor : Bertoin David Connect in order to contact the contributor
Submitted on : Thursday, September 15, 2022 - 9:49:07 AM
Last modification on : Tuesday, October 25, 2022 - 11:58:11 AM

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  • HAL Id : hal-03777742, version 1
  • ARXIV : 2209.09203

Citation

David Bertoin, Adil Zouitine, Mehdi Zouitine, Emmanuel Rachelson. LOOK WHERE YOU LOOK! SALIENCY-GUIDED Q-NETWORKS FOR VISUAL RL TASKS. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), Nov 2022, New Orleans, United States. ⟨hal-03777742⟩

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