  Despite recent success of deep network-based Reinforcement Learning (RL), it
remains elusive to achieve human-level efficiency in learning novel tasks.
While previous efforts attempt to address this challenge using meta-learning
strategies, they typically suffer from sampling inefficiency with on-policy RL
algorithms or meta-overfitting with off-policy learning. In this work, we
propose a novel meta-RL strategy to address those limitations. In particular,
we decompose the meta-RL problem into three sub-tasks, task-exploration,
task-inference and task-fulfillment, instantiated with two deep network agents
and a task encoder. During meta-training, our method learns a task-conditioned
actor network for task-fulfillment, an explorer network with a self-supervised
reward shaping that encourages task-informative experiences in
task-exploration, and a context-aware graph-based task encoder for task
inference. We validate our approach with extensive experiments on several
public benchmarks and the results show that our algorithm effectively performs
exploration for task inference, improves sample efficiency during both training
and testing, and mitigates the meta-overfitting problem.
