Averaged Soft Actor-Critic for Deep Reinforcement Learning

Averaged Soft Actor-Critic for Deep Reinforcement Learning

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With the advent of the era of artificial intelligence, deep reinforcement learning (DRL) has achieved unprecedented success in high-dimensional and large-scale artificial intelligence tasks.However, the insecurity and instability of the DRL algorithm have an important impact on its performance.The Soft Actor-Critic (SAC) algorithm uses advanced functions to update the policy and value network to j&d manufacturing website alleviate some of these problems.

However, SAC still has some problems.In order to reduce the error caused by the overestimation of SAC, we propose a new SAC algorithm called Averaged-SAC.By averaging the previously learned freetress deep twist crochet hair 14 inch action-state estimates, it reduces the overestimation problem of soft Q-learning, thereby contributing to a more stable training process and improving performance.

We evaluate the performance of Averaged-SAC through some games in the MuJoCo environment.The experimental results show that the Averaged-SAC algorithm effectively improves the performance of the SAC algorithm and the stability of the training process.