3 citations · 7 across the 5 of their papers we have counts for
4 papers · 1 filter
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning
Chen Chen, Hongyao Tang, Yi Ma +4
Pessimism is of great importance in offline reinforcement learning (RL). One broad category of offline RL algorithms fulfills pessimism by explicit or implicit behavior regularizat…
Attacking and Defending Deep Reinforcement Learning Policies
Chao Wang
Recent studies have shown that deep reinforcement learning (DRL) policies are vulnerable to adversarial attacks, which raise concerns about applications of DRL to safety-critical s…
MOORe: Model-based Offline-to-Online Reinforcement Learning
Yihuan Mao, Chao Wang, Bin Wang +1
With the success of offline reinforcement learning (RL), offline trained RL policies have the potential to be further improved when deployed online. A smooth transfer of the policy…
Towards robust and domain agnostic reinforcement learning competitions
William Hebgen Guss, Stephanie Milani, Nicholay Topin +26
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Desp…