Some Considerations on Learning to Explore via Meta-Reinforcement Learning


Nov 03, 2017 (modified: Dec 17, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and ERL2. Results are presented on a novel environment we call 'Krazy World' and a set of maze environments. We show E-MAML and ERL2 deliver better performance on tasks where exploration is important.
  • TL;DR: Modifications to MAML and RL2 that should allow for better exploration.
  • Keywords: reinforcement learning, rl, exploration, meta learning, meta reinforcement learning, curiosity