Add to ORKGThis paper presents a new method for the autonomous construction of hierarchical action and state representations in reinforcement learning, aimed at accelerating learning and extending the scope of such systems. In this approach, the agent uses information acquired while learning one task to discover subgoals for similar tasks by analyzing the learned policy using Monte Carlo sampling. The agent is able to transfer this knowledge to subsequent tasks and to accelerate learning by creating corresponding subtask policies as abstract actions (options). At the same time, the subgoal actions are used to construct a more abstract state representation using action-dependent state space partitioning, adding a new level to the state space hierarchy....
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
Reinforcement learning addresses the problem of learning to select actions in order to maximize an a...
Autonomous systems are often difficult to program. Reinforcement learning (RL) is an attractive alte...
Reinforcement learning provides a means for autonomous agents to improve their action selection stra...
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale application...
We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowle...
This paper presents a novel model of reinforcement learning agents. A feature of our learning agent ...
We intend to develop a framework that allows to determine sub goals for hierarchical reinforcement l...
Abstract. Reinforcement learning is bedeviled by the curse of dimensionality: the number of paramete...
Reinforcement learning (RL) is an area of Machine Learning (ML) concerned with learning how a softwa...
Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tai...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
Reinforcement learning addresses the problem of learning to select actions in order to maximize an a...
Autonomous systems are often difficult to program. Reinforcement learning (RL) is an attractive alte...
Reinforcement learning provides a means for autonomous agents to improve their action selection stra...
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale application...
We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowle...
This paper presents a novel model of reinforcement learning agents. A feature of our learning agent ...
We intend to develop a framework that allows to determine sub goals for hierarchical reinforcement l...
Abstract. Reinforcement learning is bedeviled by the curse of dimensionality: the number of paramete...
Reinforcement learning (RL) is an area of Machine Learning (ML) concerned with learning how a softwa...
Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tai...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...