Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks
Meta reinforcement learning (meta-RL) aims to learn a policy solving a set of training tasks simultaneously and quickly adapting to new tasks. It requires massive amounts of data drawn from training tasks to infer the common structure shared among tasks. Without heavy reward engineering, the sparse rewards in long-horizon tasks …