Reward-Driven LLM Agents Improve Autonomous Decision-Making.

A new intelligent agent workflow has been developed to tackle significant technical hurdles in current large language model (LLM) agent applications, particularly those involving complex, long-horizon planning, sparse reward signals, and dynamic interactions with environments. This architecture integrates multiple core AI paradigms, including visual, language, generative, graph, multimodal, reinforcement, and agent intelligence, moving beyond static prompting and limited perception-action loops. The core innovation lies in a Partially Observable Markov Decision Process (POMDP) routing mechanism, which is further enhanced by an internal, self-correcting reward model. This reward model critically evaluates potential decision trajectories *before* they are executed, allowing the agent to refine its actions proactively. By incorporating multimodal inputs and advanced reinforcement learning techniques like proximal policy optimization, the agent maintains long-term structural memory and dynamically adapts its reasoning pathways, effectively mitigating the accumulation of errors. Empirical evaluations in environments such as ALFWorld and WebShop demonstrated a substantial 24.5% absolute improvement in task success rate and trajectory efficiency compared to mainstream baselines like the standard ReAct framework. Ablation studies confirmed the critical role of the reward-driven critique module in reducing hallucination rates. This research offers a scalable framework for developing more robust and autonomous AI systems capable of complex, multi-step decision-making.