35 citations · 35 across the 2 of their papers we have counts for
8 papers
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search
Tuan Dam, Carlo D'Eramo, Jan Peters +1
Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex decision-making problems through the synergy of Monte-Carlo planning and Reinforcement Learning (RL). The h…
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning
Julen Urain, Anqi Li, Puze Liu +2
Reactive motion generation problems are usually solved by computing actions as a sum of policies. However, these policies are independent of each other and thus, they can have conf…
Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning
Andrew S. Morgan, Daljeet Nandha, Georgia Chalvatzaki +3
Substantial advancements to model-based reinforcement learning algorithms have been impeded by the model-bias induced by the collected data, which generally hurts performance. Mean…
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning
Pascal Klink, Hany Abdulsamad, Boris Belousov +3
Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives. For reinforcement…
Convex Regularization in Monte-Carlo Tree Search
Tuan Dam, Carlo D'Eramo, Jan Peters +1
Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making. The recent AlphaGo and AlphaZero algorithms have shown how to successfully combine…
Self-Paced Deep Reinforcement Learning
Pascal Klink, Carlo D'Eramo, Jan Peters +1
Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning. Despite empirical su…