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Hindsight Foresight Relabeling for Meta-Reinforcement Learning
Michael Wan · Jian Peng · Tanmay Gangwani
Event URL: https://openreview.net/forum?id=eOuqqpgJor »

Meta-reinforcement learning (meta-RL) algorithms allow for agents to learn new behaviors from small amounts of experience, mitigating the sample inefficiency problem in RL. However, while meta-RL agents can adapt quickly to new tasks at test time after experiencing only a few trajectories, the meta-training process is still sample-inefficient. Prior works have found that in the multi-task RL setting, relabeling past transitions and thus sharing experience among tasks can improve sample efficiency and asymptotic performance. We apply this idea to the meta-RL setting and devise a new relabeling method called Hindsight Foresight Relabeling (HFR). We construct a relabeling distribution using the combination of "hindsight", which is used to relabel trajectories using reward functions from the training task distribution, and "foresight", which takes the relabeled trajectories and computes the utility of each trajectory for each task. HFR is easy to implement and readily compatible with existing meta-RL algorithms. We find that HFR improves performance when compared to other relabeling methods on a variety of meta-RL tasks.

Author Information

Michael Wan (University of Illinois, Urbana Champaign)
Jian Peng (University of Illinois at Urbana-Champaign)
Tanmay Gangwani (University of Illinois, Urbana-Champaign)

I am a Ph.D. student in Computer Science at the University of Illinois, Urbana Champaign, supervised by Jian Peng. I'm interested in machine learning, especially Reinforcement Learning. My research is mainly focused on designing algorithms which efficiently leverage expert demonstrations for RL (imitation learning), address the exploration challenge in complex environment, and use generative modeling methods for model-based RL. For details, please visit https://tgangwani.github.io

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