Learning to Plan via a Multi-Step Policy Regression Method

• URL: http://arxiv.org/abs/2106.10075v1
• Date: Fri, 18 Jun 2021 11:51:49 GMT
• Title: Learning to Plan via a Multi-Step Policy Regression Method
• Authors: Stefan Wagner and Michael Janschek and Tobias Uelwer and Stefan Harmeling
• Abstract summary: We propose a new approach to increase inference performance in environments that require a specific sequence of actions in order to be solved. Instead of learning a policy for a single step, we want to learn a policy that can predict n actions in advance. We test our method on the MiniGrid and Pong environments and show drastic speedup during inference time by successfully predicting sequences of actions on a single observation.
• Score: 6.452233509848456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new approach to increase inference performance in environments that require a specific sequence of actions in order to be solved. This is for example the case for maze environments where ideally an optimal path is determined. Instead of learning a policy for a single step, we want to learn a policy that can predict n actions in advance. Our proposed method called policy horizon regression (PHR) uses knowledge of the environment sampled by A2C to learn an n dimensional policy vector in a policy distillation setup which yields n sequential actions per observation. We test our method on the MiniGrid and Pong environments and show drastic speedup during inference time by successfully predicting sequences of actions on a single observation.

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