159 citations · 329 across the 29 of their papers we have counts for
3 papers · 1 filter
The Potential of the Return Distribution for Exploration in RL
Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
This paper studies the potential of the return distribution for exploration in deterministic reinforcement learning (RL) environments. We study network losses and propagation mecha…
A0C: Alpha Zero in Continuous Action Space
Thomas M. Moerland, Joost Broekens, Aske Plaat +1
A core novelty of Alpha Zero is the interleaving of tree search and deep learning, which has proven very successful in board games like Chess, Shogi and Go. These games have a disc…
Monte Carlo Tree Search for Asymmetric Trees
Thomas M. Moerland, Joost Broekens, Aske Plaat +1
We present an extension of Monte Carlo Tree Search (MCTS) that strongly increases its efficiency for trees with asymmetry and/or loops. Asymmetric termination of search trees intro…