96 citations · 359 across the 71 of their papers we have counts for
3 papers · 1 filter
Safe Reinforcement Learning via Shielding
Mohammed Alshiekh, Roderick Bloem, Ruediger Ehlers +3
Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach t…
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning
Daniel S. Brown, Scott Niekum
In the field of reinforcement learning there has been recent progress towards safety and high-confidence bounds on policy performance. However, to our knowledge, no practical metho…
Data-Efficient Policy Evaluation Through Behavior Policy Search
Josiah P. Hanna, Philip S. Thomas, Peter Stone +1
We consider the task of evaluating a policy for a Markov decision process (MDP). The standard unbiased technique for evaluating a policy is to deploy the policy and observe its per…