RL Generalization

In paper1 Generalizable Imitation Learning from Observation via Inferring Goal Proximity, the idea of task structure/task information is proposed without further citation or reference. This high-level task structure generalizes to new situations and thus helps us to quickly learn the task in new situations. As for current AIRL methods: However, such learned reward functions often overfit to the expert demonstrations by learning spurious correlations between task-irrelevant features and expert/agent labels CoRL21, and thus suffer from generalization to slightly different initial and goal configurations from the ones seen in the demonstrations (e....

In paper1 Generalizable Imitation Learning from Observation via Inferring Goal Proximity, the idea of task structure/task information is proposed without further citation or reference. This high-level task structure generalizes to new situations and thus helps us to quickly learn the task in new situations. As for current AIRL methods: However, such learned reward functions often overfit to the expert demonstrations by learning spurious correlations between task-irrelevant features and expert/agent labels CoRL21, and thus suffer from generalization to slightly different initial and goal configurations from the ones seen in the demonstrations (e....

Paper Here; Official Blog Here Generalizable Imitation Learning from Observation via Inferring Goal Proximity is a NIPS2021 paper which focuses on the generalization problem of Learning from Demonstration(LfO). The idea of the paper is quite straightforward without much mathematical explanations. In this blog I will show the high-level idea and experiment setting of the paper. Preliminaries: LfO and “Goal” idea LfO is an imitation learning setting, where we cannot access the action information of experts’ demonstrations....

Paper Here; Official Blog Here Generalizable Imitation Learning from Observation via Inferring Goal Proximity is a NIPS2021 paper which focuses on the generalization problem of Learning from Demonstration(LfO). The idea of the paper is quite straightforward without much mathematical explanations. In this blog I will show the high-level idea and experiment setting of the paper. Preliminaries: LfO and “Goal” idea LfO is an imitation learning setting, where we cannot access the action information of experts’ demonstrations....

Paper reference Paper1: Towards Resolving Unidentifiability in Inverse Reinforcement Learning HERE Paper2: Identifiability in inverse reinforcement learning HERE Paper3: Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning HERE This papers are quite theoretical and not so easy to read. But they, at least for me, reveals something to do with generalization. Preliminaries: IRL & Identifiability IRL, as a subset of Imitation Learning, aims to recover the reward function of certain MDP, given the reward-free environment $E$ and an optimal agent policy $\pi$....