How deeply does Transformers in Action cover Markov decision process?

Defines stochastic control, five components, state-based policies, uncertainty, and reward maximization without developing PPO.

Markov decision process

As explained in Transformers in Action

A Markov decision process is a mathematical model for sequential decision-making in which an agent selects actions in states, receives rewards, and undergoes probabilistic transitions.

A Markov decision process (MDP) models sequential decisions in a changing environment with uncertainty. An agent observes its present state, selects an action through a policy, receives a reward, and moves toward a subsequent state. The policy depends on the present state, while probabilistic events also influence what happens next. section 5.1.1 section 5.1

An MDP organizes decision-making around five key components:

Agent — the decision-maker operating within the environment. section 5.1.1
States — the situations the agent encounters. section 5.1.1
Actions — the choices available in each state. section 5.1.1
Rewards — positive or negative feedback from actions and resulting states. section 5.1.1
Policy — the rule that selects the next action from the present state. section 5.1.1

Dog-training obstacle course

A dog acts as the agent, the obstacle course as the environment, and obstacles as states. Jumping, crawling, or sidestepping are actions; treats or gentle scolding provide rewards. The dog aims to maximize total treats across the course, illustrating how an agent uses interactions to pursue long-term reward. section 5.1.1

MDPs provide the foundation for reinforcement learning. In preference alignment, generation is treated as sequential decision-making so later optimization methods such as proximal policy optimization can favor outputs that better reflect human intent. section 5.1

How Transformers in Action covers it

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Also explained in

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Optimization Algorithms: AI techniques for design, planning, and control problemsA Markov decision process is a model of sequential decision-making defined by states, actions, transition probabilities, and rewards with future dynamics dependent only on the current state and action.
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Reinforcement Learning from Human FeedbackA Markov decision process is a mathematical framework that models states, actions, rewards, and probabilistic transitions for sequential decision making under the Markov property.
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Causal AIA Markov decision process is a sequential decision model in which an agent selects actions in states and receives rewards, with future dynamics determined by the current state and action.

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Related concepts

reinforcement learningused by · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.
Markov chaincontrasts with · A Markov chain is a stochastic process of states whose transition probabilities depend on the current state rather than the full preceding history.
Bellman equationuses · A Bellman equation recursively relates a state or action value to an immediate reward and the expected discounted value of successor states or actions.
statehas part · State is information or a configuration describing the condition of a system, entity, or computation at a particular time and potentially influencing subsequent behavior or decisions.
reward signaluses · A reward signal is an evaluative feedback value representing the desirability or payoff of an action, outcome, or state.

This explainer was generated by Manning's knowledge graph from the text of Transformers in Action. The section links point to the parts of the book it draws on.