Add to ORKGPolicy gradient methods have been widely applied in reinforcement learning. For reasons of safety and cost, learning is often conducted using a simulator. However, learning in simulation does not traditionally utilise the opportunity to improve learning by adjusting certain environment variables - state features that are randomly determined by the environment in a physical setting but controllable in a simulator. Exploiting environment variables is crucial in domains containing significant rare events (SREs), e.g., unusual wind conditions that can crash a helicopter, which are rarely observed under random sampling but have a considerable impact on expected return. We propose off environment reinforcement learning (OFFER), which addresses su...
Reinforcement learning has long been advertised as the one with the capability to intelligently mimi...
Interacting with the actual environment to acquire data is often costly and time-consuming in roboti...
Abstract — We present a framework for reinforcement learn-ing (RL) in a scenario where multiple simu...
Policy gradient methods have been widely applied in reinforcement learning. For reasons of safety an...
While reinforcement learning (RL) algorithms have been successfully applied to a wide range of probl...
Policy gradient methods ignore the potential value of adjusting environment variables: unobservable ...
In this dissertation we develop new methodologies and frameworks to address challenges in offline re...
Reinforcement learning is a family of machine learning algorithms, in which the system learns to mak...
Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems....
A reinforcement learning system with limited computational resources interacts with an unrestricted,...
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address compl...
How to achieve efficient reinforcement learning in various training environments is a central challe...
Replicating the human ability to solve complex planning problems based on minimal prior knowledge ha...
Typically in reinforcement learning, agents are trained and evaluated on the same environment. Conse...
Abstract. Policy Gradient methods are model-free reinforcement learn-ing algorithms which in recent ...
Reinforcement learning has long been advertised as the one with the capability to intelligently mimi...
Interacting with the actual environment to acquire data is often costly and time-consuming in roboti...
Abstract — We present a framework for reinforcement learn-ing (RL) in a scenario where multiple simu...
Policy gradient methods have been widely applied in reinforcement learning. For reasons of safety an...
While reinforcement learning (RL) algorithms have been successfully applied to a wide range of probl...
Policy gradient methods ignore the potential value of adjusting environment variables: unobservable ...
In this dissertation we develop new methodologies and frameworks to address challenges in offline re...
Reinforcement learning is a family of machine learning algorithms, in which the system learns to mak...
Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems....
A reinforcement learning system with limited computational resources interacts with an unrestricted,...
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address compl...
How to achieve efficient reinforcement learning in various training environments is a central challe...
Replicating the human ability to solve complex planning problems based on minimal prior knowledge ha...
Typically in reinforcement learning, agents are trained and evaluated on the same environment. Conse...
Abstract. Policy Gradient methods are model-free reinforcement learn-ing algorithms which in recent ...
Reinforcement learning has long been advertised as the one with the capability to intelligently mimi...
Interacting with the actual environment to acquire data is often costly and time-consuming in roboti...
Abstract — We present a framework for reinforcement learn-ing (RL) in a scenario where multiple simu...