Add to ORKGAbstract. This paper presents a framework allowing to tune continual explo-ration in an optimal way. It first quantifies the rate of exploration by defining the degree of exploration of a state as the probability-distribution entropy for choosing an admissible action. Then, the exploration/exploitation tradeoff is stated as a global optimization problem: find the exploration strategy that minimizes the expected cumulated cost, while maintaining fixed degrees of exploration at same nodes. In other words, “exploitation ” is maximized for constant “exploration”. This formulation leads to a set of nonlinear updating rules reminiscent of the value-iteration algorithm. Convergence of these rules to a local minimum can be proved for a stationary e...
This thesis investigates how an autonomous reinforcement learning agent can improve on an approximat...
We propose a new strategy for parallel reinforcement learning ; using this strategy, the optimal val...
Exploration is a critical component in reinforcement learning algorithms. Exploration exploitation t...
This paper presents a framework allowing to tune continual exploration in an optimal way. It first q...
This paper presents a model allowing to tune continual exploration in an optimal way by integrating ...
Exploration plays a fundamental role in any active learning system. This study evaluates the role of...
While in general trading o# exploration and exploitation in reinforcement learning is hard, under s...
International audienceMost experiments on policy search for robotics focus on isolated tasks, where ...
The impetus for exploration in reinforcement learning (RL) is decreasing uncertainty about the envir...
Abstract — In this paper we introduce an online algorithm that uses integral reinforcement knowledge...
The balance of exploration and exploitation is one of the focuses of reinforcement learning research...
The exploration/exploitation dilemma is a fundamental but often computationally intractable problem ...
Scalable and effective exploration remains a key challenge in reinforcement learning (RL). While the...
The reinforcement learning (RL) framework enables to construct controllers that try to find find an ...
In this paper, we describe methods for efficiently com-puting better solutions to control problems i...
This thesis investigates how an autonomous reinforcement learning agent can improve on an approximat...
We propose a new strategy for parallel reinforcement learning ; using this strategy, the optimal val...
Exploration is a critical component in reinforcement learning algorithms. Exploration exploitation t...
This paper presents a framework allowing to tune continual exploration in an optimal way. It first q...
This paper presents a model allowing to tune continual exploration in an optimal way by integrating ...
Exploration plays a fundamental role in any active learning system. This study evaluates the role of...
While in general trading o# exploration and exploitation in reinforcement learning is hard, under s...
International audienceMost experiments on policy search for robotics focus on isolated tasks, where ...
The impetus for exploration in reinforcement learning (RL) is decreasing uncertainty about the envir...
Abstract — In this paper we introduce an online algorithm that uses integral reinforcement knowledge...
The balance of exploration and exploitation is one of the focuses of reinforcement learning research...
The exploration/exploitation dilemma is a fundamental but often computationally intractable problem ...
Scalable and effective exploration remains a key challenge in reinforcement learning (RL). While the...
The reinforcement learning (RL) framework enables to construct controllers that try to find find an ...
In this paper, we describe methods for efficiently com-puting better solutions to control problems i...
This thesis investigates how an autonomous reinforcement learning agent can improve on an approximat...
We propose a new strategy for parallel reinforcement learning ; using this strategy, the optimal val...
Exploration is a critical component in reinforcement learning algorithms. Exploration exploitation t...