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Continuous reinforcement learning such as DDPG and A3C are widely used inrobot control and autonomous driving. However, both methods have theoreticalweaknesses. While DDPG cannot control noises in the control process, A3C doesnot satisfy the continuity conditions under the Gaussian policy. To addressthese concerns, we propose a new continues reinforcement learning method basedon stochastic differential equations and we call it Incremental ReinforcementLearning (IRL). This method not only guarantees the continuity of actionswithin any time interval, but controls the variance of actions in the trainingprocess. In addition, our method does not assume Markov control in agents'action control and allows agents to predict scene changes for action selection.With our method, agents no longer passively adapt to the environment. Instead,they positively interact with the environment for maximum rewards.

Incremental Reinforcement Learning --- a New Continuous Reinforcement Learning Frame Based on Stochastic Differential Equation methods