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Recently, many researchers on humanoid robotics are interested in quasi-passive-dynamic walking (quasi-PDW) which is similar to human walking. It is desirable that control parameters in quasi-PDW are automatically adjusted because robots often suffer from changes in their physical parameters and the surrounding environment. Reinforcement learning (RL) can be a key technology to this adaptability,...
We present methods to synthesize macroactions for agent systems and the methods are combined with SOS algorithm that learns rules for agent's behavior using reinforcement learning and evolutionary computation. To acquire useful macroactions, our methods use some kinds of numerical values evaluated in performing SOS algorithm, e.g., fitness values of actions or the number of transitions between rules...
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