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 …

Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through
their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the …

This work considers the problem of learning cooperative policies in complex, partially
observable domains without explicit communication. We extend three classes of single …

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 …

Many real-world tasks involve multiple agents with partial observability and limited
communication. Learning is challenging in these settings due to local viewpoints of agents …

Centralized Training for Decentralized Execution, where agents are trained offline using
centralized information but execute in a decentralized manner online, has gained popularity …

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

In the framework of fully cooperative multi-agent systems, independent (non-communicative)
agents that learn by reinforcement must overcome several difficulties to manage to …

We consider decentralized control of Markov decision processes and give complexity
bounds on the worst-case running time for algorithms that find optimal solutions …

Gradient-based approaches to direct policy search in reinforcement learning have received
much recent attention as a means to solve problems of partial observability and to avoid …