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A key element in the solution of reinforcement learning problems is the value function. The purpose of this function is to measure the long-term utility or value of any given state and it is important because an agent can use it to decide what to do next. A common problem in reinforcement learning when applied to systems having continuous states and action(More)
Case-based reasoning systems have traditionallybeen used to perform high-level reasoning in problem domains that can be adequately described using discrete , symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require(More)
The goal of transfer learning is to use the knowledge acquired in a set of source tasks to improve performance in a related but previously unseen target task. In this paper, we present a multi-layered architecture named CAse-Based Reinforcement Learner (CARL). It uses a novel combination of Case-Based Reasoning (CBR) and Reinforcement Learning (RL) to(More)
Georgia Tech won the OOce Cleanup Event at the 1994 AAAI Mobile Robot Competition with a multi-robot cooperating team. This paper describes the design and implementation of these reactive trash-collecting robots, including details of multiagent cooperation, color vision for the detection of perceptual object classes, temporal sequencing of behaviors for(More)
This paper describes lessons learned in the design, construction and programming of a team of three reac-tive trash-collecting robots. The robots placed first in the Office Cleanup Event at the 1994 AAAI Mobile Robot Competition. The discussion includes details of multiagent cooperation, color vision for the detection of perceptual object classes, temporal(More)