Reinforcement Learning and Dynamic Programming Using Function Approximators

• Main Authors: Busoniu, Lucian, Babuska, Robert, De Schutter, Bart, Ernst, Damien
• Format: eBook Book Web Resource
• Language: English
• Published: Boca Raton CRC Press 2010; Taylor & Francis Group
• Edition: 1
• Series: Automation and control engineering
• Subjects: Computer science; Digital control systems; Dynamic programming; Engineering, computing & technology; Ingénierie, informatique & technologie; Sciences informatiques; Approximate Policy; Policy Search; Parameter Vector; Execution Time; RL Method; Bellman Optimality Equation; Reward Function; Fuzzy Approximator; Inverted Pendulum; Continuous Action Control; Pi; Approximate DP; Bellman Equation; Inverted Pendulum Problem; Policy Improvement Step; DC Motor; Policy Iteration Algorithms; State Action Pair; Stochastic Case; Triangular MFs; Cross-entropy Method; Fuzzy Partition; RL Algorithm; Policy Improvement; Deterministic Case

While Dynamic Programming (DP) has helped solve control problems involving dynamic systems, its value was limited by algorithms that lacked practical scale-up capacity. In recent years, developments in Reinforcement Learning (RL), DP's model-free counterpart, has changed this. Focusing on conti...