Add to ORKGWe reveal a link between particle filtering methods and direct policy search reinforcement learning, and propose a novel reinforcement learning algorithm, based heavily on ideas borrowed from particle filters. A major advantage of the proposed algorithm is its ability to perform global search in policy space and thus find the globally optimal policy. We validate the approach on one- and two-dimensional problems with multiple optima, and compare its performance to a global random sampling method, and a state-of-the-art ExpectationMaximization based reinforcement learning algorithm
We consider the policy search approach to reinforcement learning. We show that if a “baseline distri...
Direct policy search is a practical way to solve reinforcement learning (RL) problems involving con...
Reinforcement learning is challenging if state and action spaces are continuous. The discretization ...
Conventional reinforcement learning algorithms for direct policy search are limited to finding only ...
Reinforcement Learning (RL) problems appear in diverse real-world applications and are gaining subst...
Gradient-based policy search is an alternative to value-function-based methods for reinforcement lea...
Policy search in reinforcement learning (RL) is a practical approach to interact directly with envir...
Continuous action policy search is currently the focus of intensive research, driven both by the rec...
International audienceWe introduce a novel approach to preference-based reinforcement learn-ing, nam...
Direct policy search is a promising reinforcement learning framework, in particular for controlling ...
We consider the problem of optimization in policy space for reinforcement learning. While a plethora...
Abstract We introduce a novel approach to preference-based reinforcement learning, namely a preferen...
Policy search is a successful approach to reinforcement learning. However, policy improvements often...
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide ...
Different from classic Supervised Learning, Reinforcement Learning (RL), is fundamentally interactiv...
We consider the policy search approach to reinforcement learning. We show that if a “baseline distri...
Direct policy search is a practical way to solve reinforcement learning (RL) problems involving con...
Reinforcement learning is challenging if state and action spaces are continuous. The discretization ...
Conventional reinforcement learning algorithms for direct policy search are limited to finding only ...
Reinforcement Learning (RL) problems appear in diverse real-world applications and are gaining subst...
Gradient-based policy search is an alternative to value-function-based methods for reinforcement lea...
Policy search in reinforcement learning (RL) is a practical approach to interact directly with envir...
Continuous action policy search is currently the focus of intensive research, driven both by the rec...
International audienceWe introduce a novel approach to preference-based reinforcement learn-ing, nam...
Direct policy search is a promising reinforcement learning framework, in particular for controlling ...
We consider the problem of optimization in policy space for reinforcement learning. While a plethora...
Abstract We introduce a novel approach to preference-based reinforcement learning, namely a preferen...
Policy search is a successful approach to reinforcement learning. However, policy improvements often...
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide ...
Different from classic Supervised Learning, Reinforcement Learning (RL), is fundamentally interactiv...
We consider the policy search approach to reinforcement learning. We show that if a “baseline distri...
Direct policy search is a practical way to solve reinforcement learning (RL) problems involving con...
Reinforcement learning is challenging if state and action spaces are continuous. The discretization ...