Artificial Intelligence: A Modern Approach

# AIMA Python file: mdp.py

```"""Markov Decision Processes (Chapter 17)

First we define an MDP, and the special case of a GridMDP, in which
states are laid out in a 2-dimensional grid.  We also represent a policy
as a dictionary of {state:action} pairs, and a Utility function as a
dictionary of {state:number} pairs.  We then define the value_iteration
and policy_iteration algorithms."""

from utils import *

class MDP:
"""A Markov Decision Process, defined by an initial state, transition model,
and reward function. We also keep track of a gamma value, for use by
algorithms. The transition model is represented somewhat differently from
the text.  Instead of T(s, a, s') being  probability number for each
state/action/state triplet, we instead have T(s, a) return a list of (p, s')
pairs.  We also keep track of the possible states, terminal states, and
actions for each state. [page 615]"""

def __init__(self, init, actlist, terminals, gamma=.9):
update(self, init=init, actlist=actlist, terminals=terminals,
gamma=gamma, states=set(), reward={})

def R(self, state):
"Return a numeric reward for this state."
return self.reward[state]

def T(state, action):
"""Transition model.  From a state and an action, return a list
of (result-state, probability) pairs."""
abstract

def actions(self, state):
"""Set of actions that can be performed in this state.  By default, a
fixed list of actions, except for terminal states. Override this
method if you need to specialize by state."""
if state in self.terminals:
return [None]
else:
return self.actlist

class GridMDP(MDP):
"""A two-dimensional grid MDP, as in [Figure 17.1].  All you have to do is
specify the grid as a list of lists of rewards; use None for an obstacle
(unreachable state).  Also, you should specify the terminal states.
An action is an (x, y) unit vector; e.g. (1, 0) means move east."""
def __init__(self, grid, terminals, init=(0, 0), gamma=.9):
grid.reverse() ## because we want row 0 on bottom, not on top
MDP.__init__(self, init, actlist=orientations,
terminals=terminals, gamma=gamma)
update(self, grid=grid, rows=len(grid), cols=len(grid))
for x in range(self.cols):
for y in range(self.rows):
self.reward[x, y] = grid[y][x]
if grid[y][x] is not None:

def T(self, state, action):
if action == None:
return [(0.0, state)]
else:
return [(0.8, self.go(state, action)),
(0.1, self.go(state, turn_right(action))),
(0.1, self.go(state, turn_left(action)))]

def go(self, state, direction):
"Return the state that results from going in this direction."
return if_(state1 in self.states, state1, state)

def to_grid(self, mapping):
"""Convert a mapping from (x, y) to v into a [[..., v, ...]] grid."""
return list(reversed([[mapping.get((x,y), None)
for x in range(self.cols)]
for y in range(self.rows)]))

def to_arrows(self, policy):
chars = {(1, 0):'>', (0, 1):'^', (-1, 0):'<', (0, -1):'v', None: '.'}
return self.to_grid(dict([(s, chars[a]) for (s, a) in policy.items()]))

Fig[17,1] = GridMDP([[-0.04, -0.04, -0.04, +1],
[-0.04, None,  -0.04, -1],
[-0.04, -0.04, -0.04, -0.04]],
terminals=[(3, 2), (3, 1)])

def value_iteration(mdp, epsilon=0.001):
"Solving an MDP by value iteration. [Fig. 17.4]"
U1 = dict([(s, 0) for s in mdp.states])
R, T, gamma = mdp.R, mdp.T, mdp.gamma
while True:
U = U1.copy()
delta = 0
for s in mdp.states:
U1[s] = R(s) + gamma * max([sum([p * U[s1] for (p, s1) in T(s, a)])
for a in mdp.actions(s)])
delta = max(delta, abs(U1[s] - U[s]))
if delta < epsilon * (1 - gamma) / gamma:
return U

def best_policy(mdp, U):
"""Given an MDP and a utility function U, determine the best policy,
as a mapping from state to action. (Equation 17.4)"""
pi = {}
for s in mdp.states:
pi[s] = argmax(mdp.actions(s), lambda a:expected_utility(a, s, U, mdp))
return pi

def expected_utility(a, s, U, mdp):
"The expected utility of doing a in state s, according to the MDP and U."
return sum([p * U[s1] for (p, s1) in mdp.T(s, a)])

def policy_iteration(mdp):
"Solve an MDP by policy iteration [Fig. 17.7]"
U = dict([(s, 0) for s in mdp.states])
pi = dict([(s, random.choice(mdp.actions(s))) for s in mdp.states])
while True:
U = policy_evaluation(pi, U, mdp)
unchanged = True
for s in mdp.states:
a = argmax(mdp.actions(s), lambda a: expected_utility(a,s,U,mdp))
if a != pi[s]:
pi[s] = a
unchanged = False
if unchanged:
return pi

def policy_evaluation(pi, U, mdp, k=20):
"""Return an updated utility mapping U from each state in the MDP to its
utility, using an approximation (modified policy iteration)."""
R, T, gamma = mdp.R, mdp.T, mdp.gamma
for i in range(k):
for s in mdp.states:
U[s] = R(s) + gamma * sum([p * U[s] for (p, s1) in T(s, pi[s])])
return U

```

 AI: A Modern Approach by Stuart Russell and Peter Norvig Modified: Jul 18, 2005