19 citations · 37 across the 22 of their papers we have counts for
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cs.LG2024
No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery
Alexander Rutherford, Michael Beukman, Timon Willi +3
What data or environments to use for training to improve downstream performance is a longstanding and very topical question in reinforcement learning. In particular, Unsupervised E…
cs.AI2024★ 2 cited
Monte Carlo Tree Search with Boltzmann Exploration
Michael Painter, Mohamed Baioumy, Nick Hawes +1
Monte-Carlo Tree Search (MCTS) methods, such as Upper Confidence Bound applied to Trees (UCT), are instrumental to automated planning techniques. However, UCT can be slow to explor…