Learning for nondeterministic models can take advantage of most of the techniques developed for probabilistic models (Chapter 10). Indeed, note that in reinforcement learning (RL), probabilities of action transitions are not needed, so RL techniques can be applied to nondeterministic models too. For instance, we can use the algorithms for Q-learning, parametric Q-learning, and deep Q-learning. However, these algorithms do not give explicit description models of actions. In this chapter, we therefore discuss some intuitions and also some challenges of how the techniques for learning deterministic action specifications could be extended to deal with nondeterministic models. Note, however, that learning lifted action schemas in nondeterministic models is still an open problem.

Temporal models are quite rich, allowing concurrency and temporal constraints to be handled. But the development of the temporal models is a bottleneck, to be eased with machine learning techniques. In this chapter, we first briefly address the problem of learning heuristics for temporal planning (Section 19.1). We then consider the issue of learning durative action schema and temporal methods (Section 19.2). The chapter outlines the proposed approaches, based on techniques seen earlier in the book, without getting into detailed descriptions of the corresponding procedures.

This chapter addresses the issues of acting with temporal models . It presents methods for handling dynamic controllability (Section 18.1), dispatching (Section 18.2), and execution and refinement of a temporal plan (Section 18.3). It proposes methods for acting with a reactive temporal refinement engine (Section 18.4), planning with Monte Carlo rollouts (Section 18.5), and integrating planning and acting (Section 18.6).

In this chapter we introduce different representations and techniques for acting with nondeterministic models: nondeterministic state transition systems (Section 11.1), automata (Section 11.2), behavior trees (Section 11.3), and Petri nets (Section 11.4).

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.