(2, 2): 'U',
(2, 3): 'L',
}
model = Model()
deltas = []
# repeat until convergence
k = 1.0
for it in range(20000):
if it % 10 == 0:
k += 0.01
alpha = ALPHA / k
biggest_change = 0
states_and_rewards = play_game(grid, policy)
for t in range(len(states_and_rewards) - 1):
s, _ = states_and_rewards[t]
s2, r = states_and_rewards[t+1]
# We update V(s) as we experience the episode
old_theta = model.theta.copy()
if grid.is_terminal(s2):
target = r
else:
target = r + GAMMA * model.predict(s2)
model.theta += alpha*(target - model.predict(s))*model.grad(s)
biggest_change = max(biggest_change, np.abs(old_theta - model.theta).sum())
deltas.append(biggest_change)
plt.plot(deltas)
(2, 3): 'U',
}
model = Model()
deltas = []
# repeat until convergence
k = 1.0
for it in range(20000):
if it % 10 == 0:
k += 0.01
alpha = ALPHA/k
biggest_change = 0
# generate an episode using pi
states_and_rewards = play_game(grid, policy)
# the first (s, r) tuple is the state we start in and 0
# (since we don't get a reward) for simply starting the game
# the last (s, r) tuple is the terminal state and the final reward
# the value for the terminal state is by definition 0, so we don't
# care about updating it.
for t in range(len(states_and_rewards) - 1):
s, _ = states_and_rewards[t]
s2, r = states_and_rewards[t+1]
# we will update V(s) AS we experience the episode
old_theta = model.theta.copy()
if grid.is_terminal(s2):
target = r
else:
target = r + GAMMA*model.predict(s2)
model.theta += alpha*(target - model.predict(s))*model.grad(s)
