def __init__(self, task, vfunc = None, tol=1e-3):
self.task = task
self.num_states = task.get_num_states()
self.gamma = task.gamma
self.tol = tol
if vfunc:
self.vfunc = vfunc
else:
self.vfunc = TabularVfunc(self.num_states)
class ValueIterationSolver(object):
'''
Vanilla value iteration for tabular environment
'''
def __init__(self, task, vfunc = None, tol=1e-3):
self.task = task
self.num_states = task.get_num_states()
self.gamma = task.gamma
self.tol = tol
if vfunc:
self.vfunc = vfunc
else:
self.vfunc = TabularVfunc(self.num_states)
def get_action(self, state):
'''Returns the greedy action with respect to the current policy'''
poss_actions = self.task.get_allowed_actions(state)
# compute a^* = \argmax_{a} Q(s, a)
best_action = None
best_val = -float('inf')
for action in poss_actions:
ns_dist = self.task.next_state_distribution(state, action)
val = 0.
for ns, prob in ns_dist:
val += prob * self.gamma * self.vfunc(ns)
if val > best_val:
best_action = action
best_val = val
elif val == best_val and random.random() < 0.5:
best_action = action
best_val = val
return best_action
def learn(self):
''' Performs value iteration on the MDP until convergence '''
while True:
# repeatedly perform the Bellman backup on each state
# V_{i+1}(s) = \max_{a} \sum_{s' \in NS} T(s, a, s')[R(s, a, s') + \gamma V(s')]
max_diff = 0.
# TODO: Add priority sweeping for state in xrange(self.num_states):
for state in self.task.env.get_valid_states():
poss_actions = self.task.get_allowed_actions(state)
best_val = 0.
for idx, action in enumerate(poss_actions):
val = 0.
ns_dist = self.task.next_state_distribution(state, action)
for ns, prob in ns_dist:
val += prob * (self.task.get_reward(state, action, ns) +
self.gamma * self.vfunc(ns))
if(idx == 0 or val > best_val):
best_val = val
diff = abs(self.vfunc(state) - best_val)
self.vfunc.update(state, best_val)
if diff > max_diff:
max_diff = diff
if max_diff < self.tol:
break
