382 citations · 666 across the 86 of their papers we have counts for
10 papers · 1 filter
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…
Continuous Monte Carlo Graph Search
Kalle Kujanpää, Amin Babadi, Yi Zhao +3
Online planning is crucial for high performance in many complex sequential decision-making tasks. Monte Carlo Tree Search (MCTS) employs a principled mechanism for trading off expl…
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…
Monte-Carlo Robot Path Planning
T. Dam, G. Chalvatzaki, J. Peters +1
Path planning is a crucial algorithmic approach for designing robot behaviors. Sampling-based approaches, like rapidly exploring random trees (RRTs) or probabilistic roadmaps, are…
Learning Progress Driven Multi-Agent Curriculum
Wenshuai Zhao, Zhiyuan Li, Joni Pajarinen
The number of agents can be an effective curriculum variable for controlling the difficulty of multi-agent reinforcement learning (MARL) tasks. Existing work typically uses manuall…
Topological Experience Replay
Zhang-Wei Hong, Tao Chen, Yen-Chen Lin +2
State-of-the-art deep Q-learning methods update Q-values using state transition tuples sampled from the experience replay buffer. This strategy often uniformly and randomly samples…
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…
State-Conditioned Adversarial Subgoal Generation
Vivienne Huiling Wang, Joni Pajarinen, Tinghuai Wang +1
Hierarchical reinforcement learning (HRL) proposes to solve difficult tasks by performing decision-making and control at successively higher levels of temporal abstraction. However…