177 citations · 236 across the 12 of their papers we have counts for
22 papers
Redeeming Intrinsic Rewards via Constrained Optimization
Eric Chen, Zhang-Wei Hong, Joni Pajarinen +1
State-of-the-art reinforcement learning (RL) algorithms typically use random sampling (e.g., -greedy) for exploration, but this method fails on hard exploration tasks like Monte…
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning
Yi Zhao, Rinu Boney, Alexander Ilin +2
Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment. However, depending on the qua…
Partially Observable Markov Decision Processes in Robotics: A Survey
Mikko Lauri, David Hsu, Joni Pajarinen
Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) prov…
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control
Abdolreza Taheri, Joni Pajarinen, Reza Ghabcheloo
The ability of Gaussian processes (GPs) to predict the behavior of dynamical systems as a more sample-efficient alternative to parametric models seems promising for real-world robo…
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…
Reinforcement Learning using Guided Observability
Stephan Weigand, Pascal Klink, Jan Peters +1
Due to recent breakthroughs, reinforcement learning (RL) has demonstrated impressive performance in challenging sequential decision-making problems. However, an open question is ho…