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arXiv · 2210.15515

Meta-Reinforcement Learning Using Model Parameters

Gabriel Hartmann·Amos Azaria
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Abstract

In meta-reinforcement learning, an agent is trained in multiple different environments and attempts to learn a meta-policy that can efficiently adapt to a new environment. This paper presents RAMP, a Reinforcement learning Agent using Model Parameters that utilizes the idea that a neural network trained to predict environment dynamics encapsulates the environment information. RAMP is constructed in two phases: in the first phase, a multi-environment parameterized dynamic model is learned. In the second phase, the model parameters of the dynamic model are used as context for the multi-environment policy of the model-free reinforcement learning agent.

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Gabriel Hartmann, Amos Azaria. 2022-10-27. Meta-Reinforcement Learning Using Model Parameters. https://arxiv.org/abs/2210.15515

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