Hindsight Foresight Relabeling for Meta-Reinforcement Learning

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. Deep Reinforcement Learning (RL) has achieved success on a wide variety of tasks, ranging from computer games to robotics. However, RL agents are typically trained on a single task and are extremely sample-inefficient, often requiring millions of samples to learn a good policy for just that one task. Ideally, RL agents should be able to utilize their prior knowledge and adapt to tasks quickly, just as humans do.