Fast active learning for pure exploration in reinforcement learning

Understand

Realistic environments often provide agents with very limited feedback.

• When the environment is initially unknown, the feedback, in the beginning, can be completely absent, and the agents may first choose to devote all their effort on exploring efficiently.
• The exploration remains a challenge while it has been addressed with many hand-tuned heuristics with different levels of generality on one side, and a few theoretically-backed exploration strategies on the other.
• Many of them are incarnated by intrinsic motivation and in particular explorations bonuses.