A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning

• URL: http://arxiv.org/abs/2106.02097v1
• Date: Thu, 3 Jun 2021 19:35:19 GMT
• Title: A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
• Authors: Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio
• Abstract summary: We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state. The design allows agents to learn to plan effectively, by attending to the relevant objects, leading to better out-of-distribution generalization.
• Score: 104.3643447579578
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state, in order to plan and to generalize better out-of-distribution. The agent's architecture uses a set representation and a bottleneck mechanism, forcing the number of entities to which the agent attends at each planning step to be small. In experiments with customized MiniGrid environments with different dynamics, we observe that the design allows agents to learn to plan effectively, by attending to the relevant objects, leading to better out-of-distribution generalization.

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