Add to ORKGInternational audienceRealistic environments often provide agents with very limited feedback. When the environment is initially unknown, the feedback, in the beginning, can be completely absent, and the agents may first choose to devote all their effort on exploring efficiently. The exploration remains a challenge while it has been addressed with many hand-tuned heuristics with different levels of generality on one side, and a few theoretically backed exploration strategies on the other. Many of them are incarnated by intrinsic motivation and in particular explorations bonuses. A common rule of thumb for exploration bonuses is to use 1/ √ n bonus that is added to the empirical estimates of the reward, where $n$ is a number of times this par...
Reinforcement learning (RL) agents need to explore their environments in order to learn optimal poli...
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we pro...
We introduce gain-based policies for exploration in ac-tive learning problems. For exploration in mu...
International audienceRealistic environments often provide agents with very limited feedback. When t...
Exploration plays a fundamental role in any active learning system. This study evaluates the role of...
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getti...
Recent Reinforcement Learning (RL) algorithms, such as R-MAX, make (with high probability) only a sm...
Over the course of the last decade, the framework of reinforcement learning has developed into a pro...
Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize cumulative reward...
How do you incentivize self-interested agents to $\textit{explore}$ when they prefer to $\textit{exp...
An important problem in reinforcement learning is the exploration-exploitation dilemma. Especially f...
In many real-world applications of reinforcement learning (RL), performing actions requires consumin...
To convey desired behavior to a Reinforcement Learning (RL) agent, a designer must choose a reward f...
Learning for exploration/exploitation in reinforcement learning We address in this thesis the origin...
none2noWhat is a good exploration strategy for an agent that interacts with an environment in the ab...
Reinforcement learning (RL) agents need to explore their environments in order to learn optimal poli...
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we pro...
We introduce gain-based policies for exploration in ac-tive learning problems. For exploration in mu...
International audienceRealistic environments often provide agents with very limited feedback. When t...
Exploration plays a fundamental role in any active learning system. This study evaluates the role of...
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getti...
Recent Reinforcement Learning (RL) algorithms, such as R-MAX, make (with high probability) only a sm...
Over the course of the last decade, the framework of reinforcement learning has developed into a pro...
Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize cumulative reward...
How do you incentivize self-interested agents to $\textit{explore}$ when they prefer to $\textit{exp...
An important problem in reinforcement learning is the exploration-exploitation dilemma. Especially f...
In many real-world applications of reinforcement learning (RL), performing actions requires consumin...
To convey desired behavior to a Reinforcement Learning (RL) agent, a designer must choose a reward f...
Learning for exploration/exploitation in reinforcement learning We address in this thesis the origin...
none2noWhat is a good exploration strategy for an agent that interacts with an environment in the ab...
Reinforcement learning (RL) agents need to explore their environments in order to learn optimal poli...
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we pro...
We introduce gain-based policies for exploration in ac-tive learning problems. For exploration in mu...