Exploring More When It Needs in Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2109.13477v1
• Date: Tue, 28 Sep 2021 04:29:38 GMT
• Title: Exploring More When It Needs in Deep Reinforcement Learning
• Authors: Youtian Guo and Qi Gao
• Abstract summary: We propose a mechanism of policy in Deep Reinforcement Learning, which is exploring more when agent needs, called Add Noise to Noise (AN2N) We use cumulative rewards to evaluate which past states the agents have not performed well, and use cosine distance to measure whether the current state needs to be explored more. We apply it to the field of continuous control tasks, such as halfCheetah, Hopper, and Swimmer, achieving considerable improvement in performance and convergence speed.
• Score: 3.442899929543427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a exploration mechanism of policy in Deep Reinforcement Learning, which is exploring more when agent needs, called Add Noise to Noise (AN2N). The core idea is: when the Deep Reinforcement Learning agent is in a state of poor performance in history, it needs to explore more. So we use cumulative rewards to evaluate which past states the agents have not performed well, and use cosine distance to measure whether the current state needs to be explored more. This method shows that the exploration mechanism of the agent's policy is conducive to efficient exploration. We combining the proposed exploration mechanism AN2N with Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC) algorithms, and apply it to the field of continuous control tasks, such as halfCheetah, Hopper, and Swimmer, achieving considerable improvement in performance and convergence speed.

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