Papers › Exchangeable Models in Meta Reinforcement Learning

Exchangeable Models in Meta Reinforcement Learning

12 Jun 2020ICML Workshop LifelongML 2020 7archive 2025-07-28

Iryna Korshunova, Jonas Degrave, Joni Dambre, Arthur Gretton, Ferenc Huszar

One recent approach to meta reinforcement learning (meta-RL) is to integrate models for task inference with models for control. The former component is often based on recurrent neural networks, which do not directly exploit the exchangeable structure of the inputs. We propose to use a lightweight, yet an expressive architecture that accounts for exchangeability. Combined with an off-policy reinforcement learning algorithm, it results in a meta-RL method that is sample-efficient, fast to train and able to quickly adapt to new test tasks as demonstrated on a couple of widely used benchmarks.

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Meta Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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