  Meta-reinforcement learning typically requires orders of magnitude more
samples than single task reinforcement learning methods. This is because
meta-training needs to deal with more diverse distributions and train extra
components such as context encoders. To address this, we propose a novel
self-supervised learning task, which we named Trajectory Contrastive Learning
(TCL), to improve meta-training. TCL adopts contrastive learning and trains a
context encoder to predict whether two transition windows are sampled from the
same trajectory. TCL leverages the natural hierarchical structure of
context-based meta-RL and makes minimal assumptions, allowing it to be
generally applicable to context-based meta-RL algorithms. It accelerates the
training of context encoders and improves meta-training overall. Experiments
show that TCL performs better or comparably than a strong meta-RL baseline in
most of the environments on both meta-RL MuJoCo (5 of 6) and Meta-World
benchmarks (44 out of 50).
