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cs.LG2024★ 1 cited
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
Bhavya Sukhija, Stelian Coros, Andreas Krause +2
Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms u…
cs.LG2023
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation
Daiki E. Matsunaga, Jongmin Lee, Jaeseok Yoon +3
One of the main challenges in offline Reinforcement Learning (RL) is the distribution shift that arises from the learned policy deviating from the data collection policy. This is o…