Averaged Soft Actor-Critic for Deep Reinforcement Learning

Averaged Soft Actor-Critic for Deep Reinforcement Learning

Blog Article

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 Beach Toy an important impact on its performance.The Soft Actor-Critic (SAC) algorithm uses advanced functions to update the policy and value network to 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 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 Dishwasher Handle Flap and the stability of the training process.