Soft Actor-Critic Algorithm in High-Dimensional Continuous Control Tasks

In this study, we implement and evaluate the Soft Actor-Critic (SAC) algorithm to address high-dimensional continuous control tasks in simulated environments. SAC, an off-policy deep reinforcement learning algorithm, is known for its sample efficiency and stability due to its maximum entropy objective, which encourages exploration by maximizing both reward and action entropy. This research focuses on applying SAC to the challenging Ant-v4 environment from the OpenAI Gym libraries. These tasks, with their complex state-action spaces, provide an ideal setting for evaluating the robustness and performance of SAC. Our experiments investigate the impact of key hyperparameters, such as the entropy weight ($\alpha$), learning rate, and reward scaling, on training stability and efficiency. Results demonstrate SAC’s ability to achieve competitive performance with improved exploration but highlight its sensitivity to hyperparameter settings, particularly the reward scale and entropy weight. We found that SAC outperformed baseline methods on complex tasks when optimally tuned but required precise adjustments for stable learning. This work provides insights into the practical implementation of SAC in continuous control applications, identifying challenges and proposing adjustments to enhance stability. The findings underscore SAC’s effectiveness in complex environments, with the potential for further refinement in hyperparameter tuning and adaptive entropy regulation.