states = grid.all_states
#V for value
V = {}
for s in states:
V[s] = 0
gamma = 1.0
while True:
biggest_change = 0
for s in states:
old_v = V[s]
if s in grid.location_to_action:
# calculate new value. given chance of each possible move form state/location
new_v = 0
p_a = 1.0 / len(grid.location_to_action[s])
for a in grid.location_to_action[s]:
grid.set_state(s)
r = grid.move(a)
new_v += p_a * (r + gamma * V[grid.current_state])
V[s] = new_v
biggest_change = max(biggest_change, np.abs(old_v - V[s]))
if biggest_change < SMALL_ENOUGH:
break
print_values(V, grid)
if s in policy:
# pick action in this case. we have fix policy
r = grid.get_reward(s, policy[s])
V[s] = r + GAMMA * V[grid.current_state]
biggest_change = max(biggest_change, np.abs(old_v - V[s]))
if biggest_change < SMALL_ENOUGH:
break
if __name__ == '__main__':
grid = negative_grid(-0.3)
print("rewards:")
print_values(grid.location_to_rewards, grid)
# intialize random policy. then update
policy = {}
for s in grid.location_to_action.keys():
policy[s] = np.random.choice(grid.location_to_action[s])
print("initial policy:")
print_policy(policy, grid)
V = initalize_V(grid)
while True:
#evaluate policy to find V
evalulate_v_for_policy(policy, grid, V)
"""
Summary: change policy for biggest V
s = s2
a = a2
#for logging only
logging_update_counts[s] = logging_update_counts.get(s, 0) + 1
log_biggest_change = max(log_biggest_change,
np.abs(log_old_qsa - Q[s][a]))
logging_deltas.append(log_biggest_change)
plt.plot(logging_deltas)
plt.show()
policy = {}
V = {}
for s in grid.location_to_action.keys():
policy[s] = get_best_action_from_q(Q, s, grid)
V[s] = Q[s][policy[s]]
# what's the proportion of time we spend updating each part of Q?
print("update counts:")
total = np.sum(list(logging_update_counts.values()))
for k, v in logging_update_counts.items():
logging_update_counts[k] = float(v) / total
print_values(logging_update_counts, grid)
print("values:")
print_values(V, grid)
print("policy:")
print_policy(policy, grid)
else:
Qs2 = getQs(model, s2)
a2, MaxQs2a2 = max_dict(Qs2)
a2 = random_action(a2, eps=0.5 / t)
model.theta += alpha * (r + gamma * MaxQs2a2 - model.predict(
s, a)) * model.grad(s, a)
s = s2
a = a2
delta = max(delta, np.abs(old_theta - model.theta).sum())
deltas.append(delta)
plt.plot(deltas)
plt.show()
#Find Policy and V function
Policy = {}
V = {}
Q = {}
for s in g.actions.keys():
Q[s] = getQs(model, s)
a, max_q = max_dict(Q[s])
Policy[s] = a
V[s] = max_q
print("Values")
print_values(V, g)
print("Policy")
print_policy(Policy, g)
first = True
for s, r in reversed(states_and_rewards):
if first:
first = False
else:
states_and_returns.append((s, G))
G = r + gamma * G
states_and_returns.reverse()
return states_and_returns
if __name__ == "__main__":
g = standard_grid()
print('rewards:')
print_values(g.rewards, g)
Policy = {
(2, 0): 'U',
(1, 0): 'U',
(0, 0): 'R',
(0, 1): 'R',
(0, 2): 'R',
(1, 2): 'U',
(2, 1): 'L',
(2, 2): 'U',
(2, 3): 'L',
}
#Intilize V(s) and returns
V = {}
