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The authors have applied reinforcement learning methods to real robot tasks in several aspects. We selected a skill of soccer as a task for a vision-based mobile robot. In this paper, we explain two of our method; (1)learning a shooting behavior, and (2)learning a shooting with avoiding an opponent. These behaviors were obtained by a robot in simulation and tested in a real environment in RoboCup-97...
In classification tasks, restricted Boltzmann machines (RBMs) have predominantly been used in the first stage, either as feature extractors or to provide initialization of neural networks. In this study, we propose a discriminative learning approach to provide a self-contained RBM method for classification, inspired by free-energy based function approximation (FE-RBM), originally proposed for reinforcement...
The main objective of a standard reinforcement learner is usually defined as maximization of a scalar reward function given externally from the environment. On the other hand, an intrinsically motivated reinforcement learner creates an intrinsic reward function from its own criteria such as curiosity, prediction error, and learning progress. This paper proposes a novel approach to deal with both intrinsic...
Coevolution has been receiving increased attention as a method for simultaneously developing the control structures of multiple agents. Our ultimate goal is the mutual development of skills through coevolution. The coevolutionary process is, however, often prone to settle into suboptimal strategies. The key to successful coevolution has thus far been unclear. This paper discusses how several robots...
Developmental learning approach by changing the internal state representation from simple to complex is promising in order for a robot to learn behaviors efficiently. We have proposed a reinforcement learning (RL) method for multiple learning modules with different state representations and algorithms. One of interesting results we showed is that a complex RL system can learn faster with the help...
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