Add to ORKGWe consider the most realistic reinforcement learning setting in which an agent starts in an unknown environment (the POMDP) and must follow one continuous and uninterrupted chain of experience with no access to resets or offline simulation. We provide algorithms for general connected POMDPs that obtain near optimal average reward. One algorithm we present has a convergence rate which depends exponentially on a certain horizon time of an optimal policy, but has no dependence on the number of (unobservable) states. The main building block of our algorithms is an implementation of an approximate reset strategy, which we show always exists in every POMDP. An interesting aspect of our algorithms is how they use this strategy when balancing ...
Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds...
Decentralized partially observable Markov decision processes (Dec-POMDPs) are a powerful tool for mo...
We address the problem of reinforcement learning in which observations may exhibit an arbitrary form...
We consider the most realistic reinforcement learning setting in which an agent starts in an unknown...
Acting in domains where an agent must plan several steps ahead to achieve a goal can be a challengin...
Planning plays an important role in the broad class of decision theory. Planning has drawn much atte...
AbstractActing in domains where an agent must plan several steps ahead to achieve a goal can be a ch...
Planning plays an important role in the broad class of decision theory. Planning has drawn much atte...
Planning plays an important role in the broad class of decision theory. Planning has drawn much atte...
We propose a new reinforcement learning algorithm for partially observable Markov decision processes...
We propose a new reinforcement learning algorithm for partially observable Markov decision processes...
Planning plays an important role in the broad class of decision theory. Planning has drawn much atte...
Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some d...
In reinforcement learning the task for an agent is to attain the best possible asymptotic reward wh...
In reinforcement learning the task for an agent is to attain the best possible asymptotic reward wh...
Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds...
Decentralized partially observable Markov decision processes (Dec-POMDPs) are a powerful tool for mo...
We address the problem of reinforcement learning in which observations may exhibit an arbitrary form...
We consider the most realistic reinforcement learning setting in which an agent starts in an unknown...
Acting in domains where an agent must plan several steps ahead to achieve a goal can be a challengin...
Planning plays an important role in the broad class of decision theory. Planning has drawn much atte...
AbstractActing in domains where an agent must plan several steps ahead to achieve a goal can be a ch...
Planning plays an important role in the broad class of decision theory. Planning has drawn much atte...
Planning plays an important role in the broad class of decision theory. Planning has drawn much atte...
We propose a new reinforcement learning algorithm for partially observable Markov decision processes...
We propose a new reinforcement learning algorithm for partially observable Markov decision processes...
Planning plays an important role in the broad class of decision theory. Planning has drawn much atte...
Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some d...
In reinforcement learning the task for an agent is to attain the best possible asymptotic reward wh...
In reinforcement learning the task for an agent is to attain the best possible asymptotic reward wh...
Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds...
Decentralized partially observable Markov decision processes (Dec-POMDPs) are a powerful tool for mo...
We address the problem of reinforcement learning in which observations may exhibit an arbitrary form...