An offline actor-critic policy improvement algorithm with historical state-action pairs

Key Points

• The actor-critic algorithm achieves significant policy improvement, demonstrating an overall enhancement in performance.
• In tests with historical state-action pairs, a 25% improvement in efficiency was noted over previous methods.
• Analysis utilizing offline learning frameworks leverages historical interactions to refine decision-making processes effectively across various scenarios, enhancing outcomes in complex environments to improve policy sonsistent results are observed in three distinct frameworks that were evaluated under similar conditions.