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Advances in Reinforcement Learning
Advances in Reinforcement Learning 82 Second, we construct the learning agent using the Q-learning which is a representative technique of reinforcement learning. Q-learning is a method to let an agent learn from delayed reward and punishment. It is designed to find a policy that maximizes for all states. The decision policy is represented by a function. The action vale function is shares among agents. The third, we concentrate on an application of Multi-agent systems to disaster relief usingdoi:10.5772/13280 fatcat:l2ify64kxrfdjn7ypojbdkppdi