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A deep deterministic policy gradient algorithm based on averaged state-action estimation
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DOI：10.1016/j.compeleceng.2022.108015.png)
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Deep Reinforcement Learning (DRL), one of the most popular research topics in artificial intelligence, has achieved a breakthrough in continuous control tasks. Nonetheless, the DRL algorithm's instability and local optimality have a bad influence impact on its performance. The Deep Deterministic Policy Gradients (DDPG) algorithm uses a soft update to slow down the target value rate of change to alleviate this problem. However, there is still a specific target approximate error variance. The variance will aggravate the degree of the data dispersion and reduce the stability of the model. This paper proposed the DDPG with averaged state-action estimation (Averaged-DDPG) algorithm. It aims to minimize the adverse effects of conflict, which calculates the action reward by averaging the estimated values of previously learned Q values, thus reducing the training process's fluctuation and improving the algorithm's performance. The evaluation results in continuous control tasks show that Averaged-DDPG can enhance the agent's learning efficiency and training balance more effectively than the original DDPG algorithm.
Keyword:
Deep reinforcement learning
Deep deterministic policy gradients
Averaged state-action estimation
Target approximate error
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4.9
论文数:
6.7K
被引数:
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