This question can seem a little bit too broad, but I am wondering what are the current state-of-the-art works on meta reinforcement learning. Can you provide me with the current state-of-the-art in this field?
One of the most recent papers on meta-RL is meta-Q-learning This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-reinforcement learning (meta-RL). MQL builds upon three simple ideas.
Q-learning is competitive with state of the art meta-RL algorithms if given access to a context variable that is a representation of the past trajectory.
Using a multi-task objective to maximize the average reward across the training tasks is an effective method to meta-train RL policies.
- past data from the meta-training replay buffer can be recycled to adapt the policy on a new task using off-policy updates
Experiments on standard continuous-control benchmarks suggest that MQL compares favorably with state of the art meta-RL algorithms