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A cooperative multi-agent system entitles some independent agents to complete complex tasks through coordination and cooperation. Since the dynamics of physical agents are so complex that the environment of learning is indeed stochastic, the paper introduces the decentralized multi-agent reinforcement learning (MARL) algorithm, named as Decentralized Concurrent Learning with Cooperative Policy Exploration...
This paper presents a distributed algorithm for mobile sensor networks to monitor the environment. With this algorithm, multiple mobile sensor nodes can collectively sample the environmental field and recover the environmental field function via machine learning approaches. The mobile sensor nodes are able to self-organise so that the distribution of mobile sensor nodes matches to the estimated environmental...
“Gain-Based Separation” is a novel heuristic that modifies the standard multiclass decision tree learning algorithm to produce forests that can describe an example or object with multiple classifications. When the information gain at a node would be higher if all examples of a particular classification were removed, those examples are reserved for another tree. In this way, the algorithm performs...
Two methods for behavior recognition are presented and evaluated. Both methods are based on the dynamic temporal difference algorithm Predictive Sequence Learning (PSL) which has previously been proposed as a learning algorithm for robot control. One strength of the proposed recognition methods is that the model PSL builds to recognize behaviors is identical to that used for control, implying that...
In many practical robotics problems, knowledge of the team configuration and capabilities is crucial in coordination of multiple heterogeneous robots. In a challenging environment with costly, sporadic, or absent communication, inferencing based on observed spatio-temporal state transitions is necessary for learning and reasoning. In this paper, we present a general purpose inference engine that takes...
Traditional approaches to programming robots are generally inaccessible to non-robotics-experts. A promising exception is the learning from demonstration paradigm. Here a policy mapping world observations to action selection is learned, by generalizing from task demonstrations by a teacher. Most learning from demonstration work to date considers data from a single teacher. In this paper, we consider...
A major issue for reinforcement learning (RL) applied to robotics is the time required to learn a new skill. While RL has been used to learn mobile robot control in many simulated domains, applications involving learning on real robots are still relatively rare. In this paper, the Least-Squares Policy Iteration (LSPI) reinforcement learning algorithm and a new model-based algorithm Least-Squares Policy...
Reinforcement learning has been commonly used in multi-robot decision making to cope with uncertainties in the environment. A shortcoming of this approach is the need for the robots to change their actions quite frequently, which is not feasible in a physical multi-robot system. This paper focuses on the development of a modified Q-learning algorithm with minimal switching of actions. By introducing...
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