A delay-robust method for enhanced real-time reinforcement learning.

In reinforcement learning, the Markov Decision Process (MDP) framework typically operates under a blocking paradigm, assuming a static environment during the agent's decision-making and stationary agent behavior while the environment executes its actions. This static model often proves inadequate for real-time tasks, as it lacks the flexibility to handle concurrent changes in both the agent's decision-making process and the environment's dynamic responses. Contemporary solutions, such as linear interpolation or state space augmentation, attempt to address the asynchronous nature of delayed states and actions in real-time environments. However, these methods frequently require precise delay measurements and may fail to fully capture the complexities of delay dynamics. However, these methods frequently require precise delay measurements and may fail to fully capture the complexities of delay dynamics. To address these challenges, we introduce a minimal information set that encapsulates concurrent information during agent-environment interactions, serving as the foundation of our real-time decision-making framework. The traditional blocking-mode MDP is then reformulated as a Minimal Information State Markov Decision Process (MISMDP), aligning more closely with the demands of real-time environments. Within this MISMDP framework, we propose the "Minimal information set for Real-time tasks using Actor-Critic" (MRAC), a general approach for addressing delay issues in real-time tasks, supported by a rigorous theoretical analysis of Q-function convergence. Extensive experiments across both discrete and continuous action space environments demonstrate that MRAC outperforms state-of-the-art algorithms, delivering superior performance and generalization in managing delays within real-time tasks.