We consider planning problems, that often arise in autonomous driving applications, in which an agent should decide on immediate actions so as to optimize a long term objective. For example, when a car tries to merge in a roundabout it should decide on an immediate acceleration/braking command, while the long term effect of the command is the success/failure of the merge. Such problems are characterized by continuous state and action spaces, and by interaction with multiple agents, whose behavior can be adversarial. We argue that dual versions of the MDP framework (that depend on the value function and the $Q$ function) are problematic for autonomous driving applications due to the non Markovian of the natural state space representation, and due to the continuous state and action spaces. We propose to tackle the planning task by decomposing the problem into two phases: First, we apply supervised learning for predicting the near future based on the present. We require that the predictor will be differentiable with respect to the representation of the present. Second, we model a full trajectory of the agent using a recurrent neural network, where unexplained factors are modeled as (additive) input nodes. This allows us to solve the long-term planning problem using supervised learning techniques and direct optimization over the recurrent neural network. Our approach enables us to learn robust policies by incorporating adversarial elements to the environment.
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
Center of Excellence for Generative AI, King Abdullah University of Science and Technology（生成人工智能卓越中心，国王 Abdullah 科学与技术大学）；The Swiss AI Lab IDSIA/USI/SUPSI, Switzerland（瑞士人工智能实验室 IDSIA/USI/SUPSI，瑞士）；The University of Southampton（南安普顿大学）；National University of Singapore（新加坡国立大学）
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
Institute for Artificial Intelligence（人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；Baozun（宝尊）
Beyond the Next Step: Variable-Length Latent World Models for Long-Horizon Planning
State Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院通用人工智能国家重点实验室）；Amazon AGI SF Lab（亚马逊AGI旧金山实验室）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents
University of Notre Dame（诺丁汉大学）；Stanford University（斯坦福大学）；Purdue University（普渡大学）；University of Edinburgh（爱丁堡大学）；University of Oxford（牛津大学）；Yale University（耶鲁大学）
