Add to ORKGWe introduce a skill discovery method for reinforcement learning in continuous domains that constructs chains of skills leading to an end-of-task reward. We demonstrate experimentally that it creates appropriate skills and achieves perfor-mance benefits in a challenging continuous domain.
A thesis presented for the degree of Doctor of Philosophy, 2017An important ability humans have is t...
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses...
Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is a...
Reinforcement learning addresses the problem of learning to select actions in order to maximize one’...
Among the most impressive of aspects of human intelligence is skill acquisition—the ability to ident...
Life-long learning of reusable, versatile skills is a key prerequisite forembodied agents that act i...
While reinforcement learning has recently been able to achieve unprecedented success, it often comes...
A major challenge in reinforcement learning is specifying tasks in a manner that is both interpretab...
Abstract. Graph-based domain representations have been used in discrete rein-forcement learning doma...
We propose an exploration method that incorporates lookahead search over basic learnt skills and the...
Applications of reinforcement learning to continuous control tasks often rely on a steady, informati...
Accepted at ECML 2020International audienceTaking inspiration from developmental learning, we presen...
Following the principle of human skill learning, robot acquiring skill is a process similar to human...
Deep reinforcement learning has greatly improved the performance of learning agent by combining the ...
Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowle...
A thesis presented for the degree of Doctor of Philosophy, 2017An important ability humans have is t...
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses...
Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is a...
Reinforcement learning addresses the problem of learning to select actions in order to maximize one’...
Among the most impressive of aspects of human intelligence is skill acquisition—the ability to ident...
Life-long learning of reusable, versatile skills is a key prerequisite forembodied agents that act i...
While reinforcement learning has recently been able to achieve unprecedented success, it often comes...
A major challenge in reinforcement learning is specifying tasks in a manner that is both interpretab...
Abstract. Graph-based domain representations have been used in discrete rein-forcement learning doma...
We propose an exploration method that incorporates lookahead search over basic learnt skills and the...
Applications of reinforcement learning to continuous control tasks often rely on a steady, informati...
Accepted at ECML 2020International audienceTaking inspiration from developmental learning, we presen...
Following the principle of human skill learning, robot acquiring skill is a process similar to human...
Deep reinforcement learning has greatly improved the performance of learning agent by combining the ...
Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowle...
A thesis presented for the degree of Doctor of Philosophy, 2017An important ability humans have is t...
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses...
Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is a...