Learning from rewards generated by a human trainer ob-serving the agent in action has been demonstrated to be an effective method for humans to teach an agent to perform challenging tasks. However, how to make the agent learn most efficiently from these kinds of human reward is still under-addressed. In this paper, we investigate the effect of providing social-network-based feedback intended to engen-der trainer competitiveness, focusing on its impact on the trainer’s behavior. The results of our user study with 85 subjects show that the agent’s social feedback can induce the trainer to train longer and give more feedback. Fur-thermore, the agent’s performance was much better when social-competitive feedback was provided. The results also s...
The TAMER framework, which provides a way for agents to learn to solve tasks using human-generated r...
In this paper, we address a relatively unexplored aspect of designing agents that learn from human t...
The TAMER framework, which provides a way for agents to learn to solve tasks using human-generated r...
Learning from rewards generated by a human trainer ob-serving the agent in action has been demonstra...
Learning from rewards generated by a human trainer ob- serving the agent in action has been demonstr...
Learning from rewards generated by a human trainer observing an agent in action has been proven to b...
Learning from rewards generated by a human trainer observing an agent in action has proven to be a p...
The transfer of training to the workplace often fails to occur. The authors argue that feedback gene...
In this work, we address a relatively unexplored aspect of designing agents that learn from human re...
Goal: Although automated social skills training has been proposed to enhance human social skills, th...
In this work, we address a relatively unexplored aspect of designing agents that learn from human re...
Agent-based social skills training systems have been gaining attention for their potential to improv...
Abstract — In order to be useful in real-world situations, it is critical to allow non-technical use...
Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative...
In this paper, we address a relatively unexplored aspect of designing agents that learn from human t...
The TAMER framework, which provides a way for agents to learn to solve tasks using human-generated r...
In this paper, we address a relatively unexplored aspect of designing agents that learn from human t...
The TAMER framework, which provides a way for agents to learn to solve tasks using human-generated r...
Learning from rewards generated by a human trainer ob-serving the agent in action has been demonstra...
Learning from rewards generated by a human trainer ob- serving the agent in action has been demonstr...
Learning from rewards generated by a human trainer observing an agent in action has been proven to b...
Learning from rewards generated by a human trainer observing an agent in action has proven to be a p...
The transfer of training to the workplace often fails to occur. The authors argue that feedback gene...
In this work, we address a relatively unexplored aspect of designing agents that learn from human re...
Goal: Although automated social skills training has been proposed to enhance human social skills, th...
In this work, we address a relatively unexplored aspect of designing agents that learn from human re...
Agent-based social skills training systems have been gaining attention for their potential to improv...
Abstract — In order to be useful in real-world situations, it is critical to allow non-technical use...
Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative...
In this paper, we address a relatively unexplored aspect of designing agents that learn from human t...
The TAMER framework, which provides a way for agents to learn to solve tasks using human-generated r...
In this paper, we address a relatively unexplored aspect of designing agents that learn from human t...
The TAMER framework, which provides a way for agents to learn to solve tasks using human-generated r...