£60.77

Reinforcement Learning & Optimal Control: Theorems, Proofs, and Python Implementations (Computational Mathematics Library)

Higher Education

Price data last checked 82 day(s) ago - refreshing...

View at Amazon

We'll watch every seller, every day. One email when your price arrives.

It has never been this cheap. We have no record of a lower price.

£61 today · cheaper than every other day in the last 3 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 9 days · 9 data points (no recent data)

Historical

Generating forecast…

Price Distribution

Price distribution over 9 days • 1 price levels

Days at Price

Price Analysis

Most common price: £61 (9 days, 100.0%)

Price range: £61 - £61

Price levels: 1 different prices over 9 days

Description

A rigorous graduate-level reference unifying dynamic programming, stochastic control, and modern reinforcement learning. Twenty-four focused chapters build from measure-theoretic probability and operator theory to cutting-edge algorithms, with complete proofs, sharp assumptions, and tight error bounds. Mathematical foundations. Borel-space MDPs, stochastic kernels, measurable policies, ergodicity, and trajectory measures; contraction mappings, span seminorms, and fixed-point theory. Dynamic programming at scale. Value and policy iteration, modified/asynchronous variants, linear programming duality and occupancy measures, performance difference lemmas, finite-time stopping rules. Long-run criteria. Average cost, the Poisson equation, ACOE, relative value iteration, Blackwell optimality, drift conditions for stability. Episodic control. Stochastic shortest path models with proper policies, boundary conditions, and convergence without discounting. Partial observability. Belief-MDPs, piecewise-linear convex value functions, Bayes filters, and stability of belief dynamics. Continuous time. HJB PDEs, verification theorems, viscosity solutions, and convergent numerical schemes. Linear systems. LQR and algebraic Riccati equations; LQG, Kalman filtering, and the separation principle. Nonlinear optimal control. Differential dynamic programming, iLQR, and model predictive control with recursive feasibility and Lyapunov stability. Exploration fundamentals. Stochastic and contextual bandits, UCB, Thompson sampling, and information-theoretic lower bounds. Stochastic approximation. Robbins–Monro, ODE method, two-time-scale analysis, Polyak–Ruppert averaging, and Markovian noise. Temporal-difference learning. TD(λ), LSTD, GTD-family methods, emphatic weighting, and off-policy stability. Function approximation theory. Projected Bellman equations, MSPBE, fitted value and Q iteration, Rademacher complexity, concentrability, and Bellman rank. Control algorithms. Q-learning, SARSA, double Q-learning, finite-time tabular rates, and divergence with approximation. Policy optimization. Policy gradient theorem, variance reduction, natural gradients, trust-region methods, monotonic improvement guarantees. Actor–critic and entropy regularization. GAE, PPO, SAC, mirror descent and primal–dual views, two-time-scale convergence. Model-based RL. System identification, adaptive LQR, OFU and PSRL, regret bounds, Dyna-style planning, and simulation lemmas. Offline evaluation and control. Importance sampling, doubly robust estimators, FQE/FQI, high-confidence bounds, and pessimism for reliability. Safety and robustness. Constrained MDPs, Lagrangian methods, CVaR and risk envelopes, control barrier functions, robust and distributionally robust RL. Multi-agent settings. Zero-sum and general-sum Markov games, Shapley operators, equilibrium computation, and decentralized learning dynamics.

Product Specifications

Format: paperback
ASIN: B0G1HV3RDS
Category: Books > Subjects > Science, Nature & Maths > Mathematics > Education > Higher Education
Domain: Amazon UK
Release Date: 08 November 2025
Listed Since: 08 November 2025

Barcode

No barcode data available

Reinforcement Learning & Optimal Control: Theorems, Proofs, and Python Implementations (Computational Mathematics Library)

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Springer Reinforcement Learning for Sequential Decision Making

Frontiers Of Intelligent Control And Information Processing

Reinforcement Learning for Sequential Decision and Optimal Control

Reinforcement Learning and Dynamic Programming Using Function Approximators: 39 (Automation and Control Engineering)

Packt Deep Reinforcement Learning Hands-On Second Edition

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning: 55 (Operations Research/Computer Science Interfaces Series, 55)

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning: 55 (Operations Research/Computer Science Interfaces Series, 55)

Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications

Decision Making: Uncertainty, Imperfection, Deliberation and Scalability: 538 (Studies in Computational Intelligence, 538)

Nonlinear System I: Proceedings of the 14th World Congress, International Federation of Automatic Control, Beijing, P.R. China, 5-9 July 1999 (IFAC ... Volumes) (IFAC Proceedings Volumes, Volume F)

Stochastic Networked Control Systems: Stabilization & Optimization

Security of Cyber-Physical Systems: State Estimation and Control

Multi-Agent Reinforcement Learning: Foundations and Modern Approaches

Proceedings of 2021 Chinese Intelligent Systems Conference: Volume II: 804 (Lecture Notes in Electrical Engineering, 804)

Proceedings of 2021 Chinese Intelligent Systems Conference: Volume I: 803 (Lecture Notes in Electrical Engineering, 803)

Model-based Reinforcement Learning: A Survey (Foundations and Trends® in Machine Learning)

Proceedings of 2020 Chinese Intelligent Systems Conference: Volume I: 705 (Lecture Notes in Electrical Engineering, 705)

Recent Advances in Control and Filtering of Dynamic Systems with Constrained Signals: 170 (Studies in Systems, Decision and Control)

Self-Learning Optimal Control of Nonlinear Systems: Adaptive Dynamic Programming Approach: 103 (Studies in Systems, Decision and Control, 103)

Control of Complex Systems: Theory and Applications

Earnings Management & Fintech: Sustainable Growth Strategy Book

Reinforcement and Systemic Machine Learning for Decision Making (IEEE Press Series on Systems Science and Engineering)

Practical Issues of Intelligent Innovations: 140 (Studies in Systems, Decision and Control, 140)

Recent Advances in Soft Computing and Cybernetics: 403 (Studies in Fuzziness and Soft Computing, 403)