Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …

Markov decision processes (MDPs) are often used to model sequential decision problems
involving uncertainty under the assumption of centralized control. However, many large …

This book presents an overview of formal decision making methods for decentralized
cooperative systems. It is aimed at graduate students and researchers in the fields of …

Reinforcement learning Marco Wiering Martijn van Otterlo (Eds.) Reinforcement Learning
State-of-the-Art ADAPTATION, LEARNING, AND OPTIMIZATION Volume 12 123 Page 2 …

Decision-theoretic planning is a popular approach to sequential decision making problems,
because it treats uncertainty in sensing and acting in a principled way. In single-agent …

Reinforcement Learning was originally developed for Markov Decision Processes (MDPs). It
allows a single agent to learn a policy that maximizes a possibly delayed reward signal in a …

Scaling decision theoretic planning to large multiagent systems is challenging due to
uncertainty and partial observability in the environment. We focus on a multiagent planning …

Recent works on multi-agent sequential decision making using decentralized partially
observable Markov decision processes have been concerned with interaction-oriented …

Decentralized partially observable Markov decision processes (Dec-POMDPs) are general
models for decentralized multi-agent decision making under uncertainty. However, they …

We propose an online algorithm for planning under uncertainty in multi-agent settings
modeled as DEC-POMDPs. The algorithm helps overcome the high computational …