Reinforcement learning is became one of the most important approaches to machine intelligence. Now RL is widely use by different research field as intelligent control, robotics and neuroscience. It provides us possible solution within unknown ...
To solve the problem of tradeoff between exploration and exploitation actions in reinforcement learning, the authors have proposed two-dimensional evaluation reinforcement learning, which distinguishes between reward and punishment evaluation forecasts. ...
Recently, deep reinforcement learning (DRL) has attracted considerable attention. The well-known deep Q-network (DQN) architecture successfully combines deep learning and Q-learning which is a representative reinforcement learning (RL) ...
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