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When multiple agents learn a task simultaneously in an environment, the learning results often become unstable. The problem is known as a concurrent learning problem and several methods have been proposed to resolve the problem so far. In this paper, we propose a new method that incorporates the expected failure probability (EFP) into the action selection strategy to give agents a kind of mutual adaptability...
The Improved Penalty Avoiding Rational Policy Making algorithm (IPARP) that can learn by a reward and a penalty. IPARP aims to find penalty rules that have a high possibility to receive a penalty. Though IPARP is effective in many cases, it needs many trial-and-error searches due to memory constraints. In this paper, a propagation algorithm of the Expected Failure Probability (EFP) is proposed to...
The penalty avoiding rational policy making algorithm (PARP) previously improved to save memory and cope with uncertainty, i.e., Improved PARP (IPARP). The efficiency of IPARP is influenced by threshold of a penalty rule or a penalty basis function γ significantly. In this paper, we propose a technique for learning γ. We show the effectiveness of our proposal using a soccer game task called “Keepaway”.
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