Hypernetworks for Zero-shot Transfer in Reinforcement Learning

• URL: http://arxiv.org/abs/2211.15457v1
• Date: Mon, 28 Nov 2022 15:48:35 GMT
• Title: Hypernetworks for Zero-shot Transfer in Reinforcement Learning
• Authors: Sahand Rezaei-Shoshtari, Charlotte Morissette, Francois Robert Hogan, Gregory Dudek, David Meger
• Abstract summary: Hypernetworks are trained to generate behaviors across a range of unseen task conditions. This work relates to meta RL, contextual RL, and transfer learning. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.
• Score: 21.994654567458017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.

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