Add to ORKGReinforcement Learning (RL) is a machine learning paradigm behind many successes in games, robotics and control applications. RL agents improve through trial-and-error, therefore undergoing a learning phase during which they perform suboptimally. Research effort has been put into optimising behaviour during this period, to reduce its duration and to maximise after-learning performance. We introduce a novel algorithm that extracts useful information from expert demonstrations (traces of interactions with the target environment) and uses it to improve performance. The algorithm detects unexpected decisions made by the expert and infers what goal the expert was pursuing. Goals are then used to bias decisions while learning. Our experiments in ...
Reinforcement Learning (RL) is the field of research focused on solving sequential decision-making t...
We consider the problem of incorporating end-user ad-vice into reinforcement learning (RL). In our s...
Sequential decision making from experience, or reinforcement learning (RL), is a paradigm that is we...
In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the explorat...
Reinforcement learning algorithms enable an agent to optimize its behavior from interacting with a s...
International audienceDuring recent years, deep reinforcement learning (DRL) has made successful inc...
Deep Reinforcement Learning has shown great progress in domains such as the Atari Arcade Learning En...
Neural networks and reinforcement learning have successfully been applied to various games, such as ...
Reinforcement learning from expert demonstrations (RLED) is the intersection of imitation learning w...
This paper presents a method to teach a robot to play Ping Pong from failed demonstrations in a high...
This thesis studies algorithms for teaching autonomous agents to complete tasks through trial and er...
Reinforcement learning (RL) is a common paradigm for learning tasks in robotics. However, a lot of e...
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent ca...
Reinforcement learning (RL) is a common paradigm for learning tasks in robotics. However, a lot of e...
Reinforcement learning is a promising framework for controlling complex vehicles with a high level o...
Reinforcement Learning (RL) is the field of research focused on solving sequential decision-making t...
We consider the problem of incorporating end-user ad-vice into reinforcement learning (RL). In our s...
Sequential decision making from experience, or reinforcement learning (RL), is a paradigm that is we...
In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the explorat...
Reinforcement learning algorithms enable an agent to optimize its behavior from interacting with a s...
International audienceDuring recent years, deep reinforcement learning (DRL) has made successful inc...
Deep Reinforcement Learning has shown great progress in domains such as the Atari Arcade Learning En...
Neural networks and reinforcement learning have successfully been applied to various games, such as ...
Reinforcement learning from expert demonstrations (RLED) is the intersection of imitation learning w...
This paper presents a method to teach a robot to play Ping Pong from failed demonstrations in a high...
This thesis studies algorithms for teaching autonomous agents to complete tasks through trial and er...
Reinforcement learning (RL) is a common paradigm for learning tasks in robotics. However, a lot of e...
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent ca...
Reinforcement learning (RL) is a common paradigm for learning tasks in robotics. However, a lot of e...
Reinforcement learning is a promising framework for controlling complex vehicles with a high level o...
Reinforcement Learning (RL) is the field of research focused on solving sequential decision-making t...
We consider the problem of incorporating end-user ad-vice into reinforcement learning (RL). In our s...
Sequential decision making from experience, or reinforcement learning (RL), is a paradigm that is we...
