1 citations · 1 across the 9 of their papers we have counts for
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Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
Hongyao Tang, Glen Berseth
Deep neural networks provide Reinforcement Learning (RL) powerful function approximators to address large-scale decision-making problems. However, these approximators introduce cha…
RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning
Mingqi Yuan, Roger Creus Castanyer, Bo Li +3
Extrinsic rewards can effectively guide reinforcement learning (RL) agents in specific tasks. However, extrinsic rewards frequently fall short in complex environments due to the si…
Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning
Adriana Hugessen, Roger Creus Castanyer, Faisal Mohamed +1
Both entropy-minimizing and entropy-maximizing (curiosity) objectives for unsupervised reinforcement learning (RL) have been shown to be effective in different environments, depend…