Add to ORKGIn this study, we investigate the effects of conditioning Independent Q-Learners (IQL) not solely on the individual action-observation history, but additionally on the sufficient plan-time statistic for Decentralized Partially Observable Markov Decision Processes. In doing so, we attempt to address a key shortcoming of IQL, namely that it is likely to converge to a Nash Equilibrium that can be arbitrarily poor. We identify a novel exploration strategy for IQL when it conditions on the sufficient statistic, and furthermore show that sub-optimal equilibria can be escaped consistently by sequencing the decision-making during learning. The practical limitation is the exponential complexity of both the sufficient statistic and the decision rules...
This thesis involves the use of a reinforcement learning algorithm (RL) called Q-learning to train a...
Reinforcement learning is a promising technique for learning agents to adapt their own strategies in...
Following the novel paradigm developed by Van Roy and coauthors for reinforcement learning in arbitr...
In this study, we investigate the effects of conditioning Independent Q-Learners (IQL) not solely on...
The Decentralized Partially Observable Markov Decision Process is a commonly used framework to forma...
Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Ma...
We propose a multi-agent reinforcement learning dynamics, and analyze its convergence properties in ...
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning contr...
© 2016 The Authors and IOS Press. Q-learning associates states and actions of a Markov Decision Proc...
A very general framework for modeling uncertainty in learning environments is given by Partially Obs...
Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge to opti...
Recently, there have been several attempts to design multiagent Q-learning algorithms that learn equ...
Abstract — Q-learning is a technique used to compute an opti-mal policy for a controlled Markov chai...
To solve partially observable Markov decision problems, we introduce HQ-learning, a hierarchical ext...
This article investigates the performance of independent reinforcement learners in multi-agent games...
This thesis involves the use of a reinforcement learning algorithm (RL) called Q-learning to train a...
Reinforcement learning is a promising technique for learning agents to adapt their own strategies in...
Following the novel paradigm developed by Van Roy and coauthors for reinforcement learning in arbitr...
In this study, we investigate the effects of conditioning Independent Q-Learners (IQL) not solely on...
The Decentralized Partially Observable Markov Decision Process is a commonly used framework to forma...
Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Ma...
We propose a multi-agent reinforcement learning dynamics, and analyze its convergence properties in ...
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning contr...
© 2016 The Authors and IOS Press. Q-learning associates states and actions of a Markov Decision Proc...
A very general framework for modeling uncertainty in learning environments is given by Partially Obs...
Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge to opti...
Recently, there have been several attempts to design multiagent Q-learning algorithms that learn equ...
Abstract — Q-learning is a technique used to compute an opti-mal policy for a controlled Markov chai...
To solve partially observable Markov decision problems, we introduce HQ-learning, a hierarchical ext...
This article investigates the performance of independent reinforcement learners in multi-agent games...
This thesis involves the use of a reinforcement learning algorithm (RL) called Q-learning to train a...
Reinforcement learning is a promising technique for learning agents to adapt their own strategies in...
Following the novel paradigm developed by Van Roy and coauthors for reinforcement learning in arbitr...