Add to ORKGAn opportunistic agent need not only to identify, learn to recognize and to exploit opportunities. This is of particular interest in complex environments, where an agent is unable to attain full overview of the situation. Real-world environments are riddled with uncertainty, there are changes taking place everywhere, agents have severely limited observability, and there is no realistic way to evaluate all possible states/actions. We adapt a proven model of temporarily suspending goals (instead of permanently discarding them), should the goals constraints become invalidated. We propose a conceptual framework that uses reinforcement learning on observations in a partially observable Markov decision process for learning to recognize future opp...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
People are efficient when they make decisions under uncertainty, even when their decisions have long...
AbstractActing in domains where an agent must plan several steps ahead to achieve a goal can be a ch...
In this paper, we describe how techniques from reinforcement learning might be used to approach the ...
In applying reinforcement learning to agents acting in the real world we are often faced with tasks ...
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address compl...
Acting in domains where an agent must plan several steps ahead to achieve a goal can be a challengin...
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address compl...
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address compl...
My research activity focuses on the integration of acting, learning and planning. The main objective...
We study using reinforcement learning in particular dynamic environ-ments. Our environments can cont...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
People are efficient when they make decisions under uncertainty, even when their decisions have long...
AbstractActing in domains where an agent must plan several steps ahead to achieve a goal can be a ch...
In this paper, we describe how techniques from reinforcement learning might be used to approach the ...
In applying reinforcement learning to agents acting in the real world we are often faced with tasks ...
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address compl...
Acting in domains where an agent must plan several steps ahead to achieve a goal can be a challengin...
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address compl...
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address compl...
My research activity focuses on the integration of acting, learning and planning. The main objective...
We study using reinforcement learning in particular dynamic environ-ments. Our environments can cont...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...
Reinforcement Learning (RL) has emerged as an effective approach to address a variety of complex con...