Add to ORKGReinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapproximationmethods,suchas recurrent neural nets or history-based state abstraction, perform better than their memory-less counterparts, due to the fact that function approximation in Markov decision processes (MDP) can be viewed as inducing a Partially observable MDP. However, there has been little formal analysis of such history-based algorithms, as most existing frameworks focus exclusively on memory-less features. In this paper, we introduce a theoretical framework for studying the behaviour of RL algorithms that learn to control an MDP using history-based feature abstraction mappings. Furthermore, we use this framework to design a practical RL algorithm and we numerical...
The application of reinforcement learning (RL) algorithms is often hindered by the combinatorial exp...
The application of reinforcement learning (RL) algorithms is often hindered by the combinatorial exp...
To operate effectively in complex environments learning agents require the ability to form useful ab...
A very general framework for modeling uncertainty in learning environments is given by Partially Obs...
Feature reinforcement learning was introduced five years ago as a principled and practical approach ...
AbstractTechniques based on reinforcement learning (RL) have been used to build systems that learn t...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observatio...
Reinforcement Learning (RL) is currently an active research area of Artificial Intelligence (AI) in ...
We extend the Q-learning algorithm from the Markov Decision Process setting to pr...
We consider the problem of learning a policy for a Markov decision process consistent with data capt...
Reinforcement Learning (RL) is an area concerned with learning how to act in an environment to reach...
In this paper, we give a brief review of Markov Decision Processes (MDPs), and how Reinforcement Lea...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
AbstractTechniques based on reinforcement learning (RL) have been used to build systems that learn t...
State abstraction and value function approximation are essential tools for the feasibility of sequen...
The application of reinforcement learning (RL) algorithms is often hindered by the combinatorial exp...
The application of reinforcement learning (RL) algorithms is often hindered by the combinatorial exp...
To operate effectively in complex environments learning agents require the ability to form useful ab...
A very general framework for modeling uncertainty in learning environments is given by Partially Obs...
Feature reinforcement learning was introduced five years ago as a principled and practical approach ...
AbstractTechniques based on reinforcement learning (RL) have been used to build systems that learn t...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observatio...
Reinforcement Learning (RL) is currently an active research area of Artificial Intelligence (AI) in ...
We extend the Q-learning algorithm from the Markov Decision Process setting to pr...
We consider the problem of learning a policy for a Markov decision process consistent with data capt...
Reinforcement Learning (RL) is an area concerned with learning how to act in an environment to reach...
In this paper, we give a brief review of Markov Decision Processes (MDPs), and how Reinforcement Lea...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
AbstractTechniques based on reinforcement learning (RL) have been used to build systems that learn t...
State abstraction and value function approximation are essential tools for the feasibility of sequen...
The application of reinforcement learning (RL) algorithms is often hindered by the combinatorial exp...
The application of reinforcement learning (RL) algorithms is often hindered by the combinatorial exp...
To operate effectively in complex environments learning agents require the ability to form useful ab...