马尔科夫决策过程的Matlab程序，包括一些例程-Markov Decision Process.rar

function [T, T2] = mk_grid_world(nrows, ncols, psucc_act, obstacle, terminal, absorb, wrap_around, noop) % MK_GRID_WORLD Make the transition matrix for a grid world with stochastic actions. % T = mk_grid_world(nrows, ncols, psucc_act, obstacle, terminal, absorb, wrap_around) % % An action succeeds with prob. p = psucc_act and moves in a perpendicular direction with prob. 1-p. % If you try to move into an obstacle, you stay where you are. % % Input: % obstacle(i,j)=p means there is an obstacle at i,j with prob p. % terminal(i,j)=1 if i,j is a terminal state % If absorb = 1, terminal states jump to a unique absorbing state. % If absorb = 0, terminal states are themselves absorbing. % If noop = 1, we allow a no-op action that has no affect. % If wrap_around = 1, we use toroidal boundary conditions. % If wrap_around = 0, we cannot go past the boundaries. % % Output: % T(s,a,s') = Pr(s' | s, a), where s in [1, 2, ..., nrows*ncols]. % States are numbered as in the following example % 1 5 9 % 2 6 10 % 3 7 11 % 4 8 12 % and actions are numbered as North(1), South (2), East(3), West (4) N = 1; E = 2; S = 3; W = 4; nact = 4; nstates = nrows*ncols; % T1{N}(i,j)=k means s->k when we go north, where s = encode_xy(i,j) % This assumes no noise and no obstacles. % % Example: nrows = 4, ncols = 3, wrap_around = 1 % M = % 1 5 9 % 2 6 10 % 3 7 11 % 4 8 12 % T1{N} = % 4 8 12 % 1 5 9 % 2 6 10 % 3 7 11 % This means that state 1 goes to 4, state 2 goes to 1, etc. if wrap_around rows{N} = [nrows 1:nrows-1]; cols{N} = 1:ncols; rows{E} = 1:nrows; cols{E} = [2:ncols 1]; rows{S} = [2:nrows 1]; cols{S} = 1:ncols; rows{W} = 1:nrows; cols{W} = [ncols 1:ncols-1]; else rows{N} = [1 1:nrows-1]; cols{N} = 1:ncols; rows{E} = 1:nrows; cols{E} = [2:ncols ncols]; rows{S} = [2:nrows nrows]; cols{S} = 1:ncols; rows{W} = 1:nrows; cols{W} = [1 1:ncols-1]; end M = reshape(1:nrows*ncols, [nrows ncols]); T1 = cell(1, nact); for i=1:4 T1{i} = M(rows{i}, cols{i}); end % T2{N}(s,s')=r means s->s' with prob r when we go north. % This is computed by assigning prob mass p to the cell north of s, % and q to the cells to the east and west of s, where % p = psucc_act, q = (1-p)/2. % If there is an obstacle in a target cell, that amount of prob mass % is added to the self-loop prob. % % Example % obstacle = % 0 0 0 % 0 1 0 % 0 0 0 % 0 0 1 % p = 0.8, q = 0.1 % T2{N}(1,:) = % 0 0.1000 0.1000 % 0 0 0 % 0 0 0 % 0.8000 0 0 % % i.e., P(1->5) = P(1->9) = q, P(1->4) = p since going north from 1 wraps round to 4 % % reshape(T2{1}(7,:), 4,3) % 0 0 0 % 0 0 0 % 0.1000 0.8000 0.1000 % 0 0 0 % i.e., P(7->3) = P(7->11) = q, P(7->7) = p since going north from 7 hits the obstacle at 6 % % If there is uncertainty about the obstacle map, the prob of a move gets reduced by a factor % of r, where r = prob. destn is UNoccupied dir = [N E W; E N S; S E W; W N S]; p = psucc_act; q = (1-p)/2; prob = [p q q; p q q; p q q; p q q]; T2 = cell(1,nact); for i=1:4 T2{i} = zeros(nstates, nstates); for j=1:3 d = dir(i,j); p = prob(i,j); tmp = subv2ind([nstates nstates], [(1:nstates)' T1{d}(:)]); prob_unoccupied = (1-obstacle(rows{d}, cols{d})); T2{i}(tmp) = T2{i}(tmp)' + p * prob_unoccupied(:); end tmp = subv2ind([nstates nstates], [(1:nstates)' M(:)]); T2{i}(tmp) = T2{i}(tmp)' + ones(nstates, 1) - sum(T2{i},2); end term = M(logical(terminal(:))); if absorb T = zeros(nstates + 1, nact, nstates + 1); for i=1:4 T(1:nstates, i, 1:nstates) = T2{i}; end astate = nstates + 1; T(astate, :, astate) = 1; T(term, :, :) = 0; T(term, :, astate) = 1; else tmp = subv2ind([nstates nstates], [term term]); T = zeros(nstates, nact, nstates); for i=1:4 T2{i}(term, :) = 0; T2{i}(tmp) = 1; % equivalent to T2{i}(term(j),term(j)) = 1 for all j T(:,i,:) = T2{i}; end end if noop nact = 5; Told = T; ns = size(Told, 1); % might be nstates or nstates+1 T = zeros(ns, nact, ns); T(:,1:nact-1,:) = Told; T(:,nact,:) = eye(ns); end