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A survey of inverse reinforcement learning: Challenges, methods and progress
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 …
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …
Cooperative multi-agent learning: The state of the art
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 …
their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the …
Cooperative multi-agent control using deep reinforcement learning
This work considers the problem of learning cooperative policies in complex, partially
observable domains without explicit communication. We extend three classes of single …
observable domains without explicit communication. We extend three classes of single …
[ספר][B] A concise introduction to decentralized POMDPs
FA Oliehoek, C Amato - 2016 - Springer
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 …
cooperative systems. It is aimed at graduate students and researchers in the fields of …
Deep decentralized multi-task multi-agent reinforcement learning under partial observability
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 …
communication. Learning is challenging in these settings due to local viewpoints of agents …
Contrasting centralized and decentralized critics in multi-agent reinforcement learning
Centralized Training for Decentralized Execution, where agents are trained offline using
centralized information but execute in a decentralized manner online, has gained popularity …
centralized information but execute in a decentralized manner online, has gained popularity …
Reinforcement learning
Reinforcement learning Marco Wiering Martijn van Otterlo (Eds.) Reinforcement Learning
State-of-the-Art ADAPTATION, LEARNING, AND OPTIMIZATION Volume 12 123 Page 2 …
State-of-the-Art ADAPTATION, LEARNING, AND OPTIMIZATION Volume 12 123 Page 2 …
Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems
In the framework of fully cooperative multi-agent systems, independent (non-communicative)
agents that learn by reinforcement must overcome several difficulties to manage to …
agents that learn by reinforcement must overcome several difficulties to manage to …
The complexity of decentralized control of Markov decision processes
We consider decentralized control of Markov decision processes and give complexity
bounds on the worst-case running time for algorithms that find optimal solutions …
bounds on the worst-case running time for algorithms that find optimal solutions …
Proactive resource management for LTE in unlicensed spectrum: A deep learning perspective
Performing cellular long term evolution (LTE) communications in unlicensed spectrum using
licensed assisted access LTE (LTE-LAA) is a promising approach to overcome wireless …
licensed assisted access LTE (LTE-LAA) is a promising approach to overcome wireless …