Long Horizon Decision Making

Long-horizon decision-making focuses on enabling artificial agents to effectively plan and execute complex tasks requiring numerous sequential actions over extended timeframes. Current research emphasizes hierarchical approaches, often combining large language models (LLMs) for high-level planning with reinforcement learning (RL) for low-level control and adaptation to unforeseen circumstances, sometimes incorporating world models to predict future states. These advancements are improving the performance of embodied AI agents in simulated and real-world environments, with applications ranging from robotics to autonomous driving, by enabling more robust and efficient planning in complex, uncertain situations.