By Richard S. Sutton, Andrew G. Barto
Reinforcement studying, some of the most lively learn parts in synthetic intelligence, is a computational method of studying wherein an agent attempts to maximise the entire volume of gift it gets whilst interacting with a complicated, doubtful atmosphere. In Reinforcement studying, Richard Sutton and Andrew Barto supply a transparent and easy account of the most important rules and algorithms of reinforcement studying. Their dialogue levels from the historical past of the field's highbrow foundations to the latest advancements and functions. the single helpful mathematical history is familiarity with user-friendly techniques of probability.The booklet is split into 3 elements. half I defines the reinforcement studying challenge by way of Markov selection techniques. half II presents easy resolution tools: dynamic programming, Monte Carlo equipment, and temporal-difference studying. half III offers a unified view of the answer tools and contains synthetic neural networks, eligibility lines, and making plans; the 2 ultimate chapters current case reviews and examine the way forward for reinforcement learning.
Read Online or Download Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) PDF
Similar artificial intelligence books
Stochastic neighborhood seek (SLS) algorithms are one of the such a lot renowned and winning ideas for fixing computationally tough difficulties in lots of parts of computing device technology and operations learn, together with propositional satisfiability, constraint delight, routing, and scheduling. SLS algorithms have additionally turn into more and more renowned for fixing hard combinatorial difficulties in lots of program parts, resembling e-commerce and bioinformatics.
Neural Networks for Pattern Recognition
This can be the 1st complete remedy of feed-forward neural networks from the viewpoint of statistical trend reputation. After introducing the fundamental recommendations, the e-book examines concepts for modeling chance density services and the houses and benefits of the multi-layer perceptron and radial foundation functionality community types.
Handbook of Temporal Reasoning in Artificial Intelligence, Volume 1
This assortment represents the first reference paintings for researchers and scholars within the region of Temporal Reasoning in man made Intelligence. Temporal reasoning has an essential function to play in lots of components, relatively synthetic Intelligence. but, before, there was no unmarried quantity accumulating jointly the breadth of labor during this region.
Programming Multi-Agent Systems in AgentSpeak using Jason
Jason is an Open resource interpreter for a longer model of AgentSpeak – a logic-based agent-oriented programming language – written in Java™. It permits clients to construct complicated multi-agent platforms which are able to working in environments formerly thought of too unpredictable for desktops to deal with.
Additional info for Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
Example text
Trial 17 was 109 steps. Trial 18 was 38 steps. Trial 19 was 13 steps. Trial 20 was 144 steps. Trial 21 was 41 steps. Trial 22 was 323 steps. Trial 23 was 172 steps. Trial 24 was 33 steps. Trial 25 was 1166 steps. Trial 26 was 905 steps. Trial 27 was 874 steps. Trial 28 was 758 steps. Trial 29 was 758 steps. Trial 30 was 756 steps. Trial 31 was 165 steps. Trial 32 was 176 steps. Trial 33 was 216 steps. Trial 34 was 176 steps. Trial 35 was 185 steps. Trial 36 was 368 steps. Trial 37 was 274 steps.
Trial 67 was 16815 steps. Trial 68 was 21896 steps. Trial 69 was 11566 steps. Trial 70 was 22968 steps. Trial 71 was 17811 steps. Trial 72 was 11580 steps. Trial 73 was 16805 steps. Trial 74 was 16825 steps. Trial 75 was 16872 steps. Trial 76 was 16827 steps. Trial 77 was 9777 steps. Trial 78 was 19185 steps. Trial 79 was 98799 steps. 0)) (loop for k below 1000 do (loop for x from 1 below (- states 1) do (setf (aref V- x) (loop for a below 4 maximize (full-backup x a))) do (multiple-value-bind (x y) (xy-from-state x) (setf (aref (aref Vk k) x y) (aref V x)))) do (ut::swap V V-)) (loop for state below states do (multiple-value-bind (x y) (xy-from-state state) (setf (aref VV y x) (aref V state)))) (sfa VV)) (defun sfa (array) "Show Floating-Point Array" (cond ((= 1 (array-rank array)) (loop for e across array do (format t "~8,3F" e))) (t (loop for i below (array-dimension array 0) do (format t "~%") (loop for j below (array-dimension array 1) do (format t "~8,3F" (aref array i j))))))) (defun full-backup (x a) (let (r y) (cond ((off-grid x a) (setq r -1) (setq y x)) (t (setq r -1) (setq y (next-state x a)))) (+ r (* gamma (aref V y))))) (defun off-grid (state a) (multiple-value-bind (x y) (xy-from-state state) (case a (0 (incf y) (>= y rows)) (1 (incf x) (>= x columns)) (2 (decf y) (< y 0)) (3 (decf x) (< x 0))))) (defun next-state (state a) (multiple-value-bind (x y) (xy-from-state state) (case a (0 (incf y)) (1 (incf x)) (2 (decf y)) (3 (decf x))) (state-from-xy x y))) (defun state-from-xy (x y) (+ y (* x columns))) (defun xy-from-state (state) (truncate state columns)) (defun truncate-last-values () (loop for state from 1 below (- states 1) do (multiple-value-bind (x y) (xy-from-state state) (setf (aref (aref Vk 999) x y) (round (aref (aref Vk 999) x y)))))) ;;; Jack's car rental problem.
Trial 30 was 756 steps. Trial 31 was 165 steps. Trial 32 was 176 steps. Trial 33 was 216 steps. Trial 34 was 176 steps. Trial 35 was 185 steps. Trial 36 was 368 steps. Trial 37 was 274 steps. Trial 38 was 323 steps. Trial 39 was 244 steps. Trial 40 was 352 steps. Trial 41 was 366 steps. Trial 42 was 622 steps. Trial 43 was 236 steps. Trial 44 was 241 steps. Trial 45 was 245 steps. Trial 46 was 250 steps. Trial 47 was 346 steps. Trial 48 was 384 steps. Trial 49 was 961 steps. Trial 50 was 526 steps.