Josh Merel

RL Unplugged: Benchmarks for Offline Reinforcement Learning.

2020 · arXiv (Cornell University)

Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to learn policies from offline datasets, thus overcoming concerns associated with …

Evaluating model-based planning and planner amortization for continuous control

2021 · arXiv (Cornell University)

There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We …