In the past, techniques for natural language translation were not very relevant for acting and planning systems. However, with the recent advent of large language models and their various multimodal extensions into foundation models, this is no longer the case. This last part introduces large language models and their potential benefits in acting, planning, and learning. It discusses the perceiving, monitoring, and goal reasoning functions for deliberation.

Learning to act with probabilistic models is the area of reinforcement learning (RL), the topic of this chapter. RL in some ways parallels the adaptation mechanisms of natural beings to their environment, relying on feedback mechanisms and extending the homeostasis regulations to complex behaviors. With continual learning, an actor can cope with a continually changing environment.This chapter first introduces the main principles of reinforcement learning. It presents a simple Q-learning RL algorithm. It shows how to generalize a learned relation with a parametric representation. it introduces neural network methods, which play a major in learning and are needed for deep RL (Section 10.5) and policy-based RL (Section 10.6). The issues of aided reinforcement learning with shaped rewards, imitation learning, and inverse reinforcement learning are addressed next. Section 10.8 is about probabilistic planning and RL.

This part of the book is devoted to acting, planning, and learning with operational models of actions expressed with a hierarchical task-oriented representation. Operational models are valuable for acting. They allow for detailed descriptions of complex actions handling dynamic environments with exogenous events. The representation relies on hierarchical refinement methods that describe alternative ways to handle tasks and react to events. A method can be any complex algorithm, decomposing a task into subtasks and primitive actions. Subtasks are refined recursively. Actions trigger the execution of sensory-motor procedures in closed loops that query and change the world stochastically.

Task and motion planning (TAMP) problems combine abstract causal relations from preconditions to effects with computational geometry, kinematics, and dynamics. This chapter is about the integration of planning for motion/manipulation with planning for abstract actions. It introduces the main sampling-based algorithms for motion planning. Manipulation planning is subsequently introduced. A few approaches specific to TAMP are then presented.

This chapter is about planning approaches with explicit time in the descriptive and operational models of actions, as well as in the models of the expected evolution of the world not caused by the actor. It describes a planning algorithm that handles durative and concurrent activities with respect to a predicted dynamics. Section 17.1 presents a knowledge representation for modeling actions and tasks with temporal variables using temporal refinement methods. Temporal plans and planning problems are defined as chronicles, i.e., collections of assertions and tasks with explicit temporal constraints. A planning algorithm with temporal refinement methods is developed in Section 17.2. The basic techniques for managing temporal and domain constraints are then presented in Section 17.3.

This chapter is about two key aspects of learning with deterministic models: learning heuristics to speed up the search for a solution plan and the automated synthesis of the model itself. We discuss how to learn heuristics for exploring parts of the search space that are more likely to lead to solutions. We then address the problem of how to learn a deterministic model, with a focus on learning action schemas.