def test_td0_method(alpha=0.1, gamma=0.9, eps=0.1):
game = StandardGrid()
print_values(game.rewards, game)
policy = {
## GridWorldMove.up, GridWorldMove.down, GridWorldMove.right, GridWorldMove.left
(0, 0): [0, 0, 1, 0],
(0, 1): [1, 0, 0, 0],
(0, 2): [1, 0, 0, 0],
(1, 0): [0, 0, 1, 0],
(1, 2): [0, 0, 1, 0],
(2, 0): [0, 0, 1, 0],
(2, 1): [0, 0, 1, 0],
(2, 2): [0, 0, 1, 0],
(3, 2): [1, 0, 0, 0]
}
print("Policy:")
print_policy_beautifly(policy, game)
V = {}
states = game.allStates()
for s in states:
V[s] = 0
for t in xrange(100000):
s_and_rs = play_game(game, policy, False, eps)
for i in xrange(len(s_and_rs) - 1):
state, _ = s_and_rs[i]
s_plus_one, reward = s_and_rs[i + 1]
V[state] = V[state] + alpha * (reward + gamma * V[s_plus_one] -
V[state])
print("Policy:")
print_policy_beautifly(policy, game)
print("Values:")
print_values(V, game)
def testMonteCarloValuesEvaluation(windy=False):
gamma = 0.9
eps = 0.2
game = StandardGrid()#NegativeGrid(-0.25)
## This is to make things real
#generateRandomPolicy()
policy = generateRandomlyDeterministicPolicy(game)
V = {}
returns = {}
states = game.allStates()
seenStates = {}
for s in states:
if s in game.actions.keys():
returns[s] = []
seenStates.update({s : 0})
else:
V[s] = 0
for t in range(0,10000):
sAr = playGame(game, policy, gamma, windy, eps)
for s, g in sAr:
if s in game.actions.keys():
if seenStates[s] == 0:
returns[s].append(g)
V[s] = np.mean(returns[s])
seenStates.update({s : 1})
print_values(V, game)
def test_td0_approximation_method(alpha=0.1,
gamma=0.9,
eps=0.1,
itterations=10000):
game = StandardGrid()
print_values(game.rewards, game)
policy = {
## GridWorldMove.up, GridWorldMove.down, GridWorldMove.right, GridWorldMove.left
(0, 0): [0, 0, 1, 0],
(0, 1): [1, 0, 0, 0],
(0, 2): [1, 0, 0, 0],
(1, 0): [0, 0, 1, 0],
(1, 2): [0, 0, 1, 0],
(2, 0): [0, 0, 1, 0],
(2, 1): [0, 0, 1, 0], #(2, 1): [0, 0, 1 , 0],
(2, 2): [0, 0, 1, 0], #(2, 2): [0, 0, 1 , 0],
(3, 2): [1, 0, 0, 0]
}
print("Policy:")
print_policy_beautifly(policy, game)
states = game.allStates()
# Let's create a state
model = LinearState()
deltas = [] ## Debug only
for t in xrange(itterations):
s_and_rs = play_game(game, policy, False, eps)
biggest_change = 0
alpha = alpha / 1.0001
for i in xrange(len(s_and_rs) - 1):
state, _ = s_and_rs[i]
s_plus_one, reward = s_and_rs[i + 1]
if game.isTerminal(s_plus_one):
target = reward
else:
target = reward + gamma * model.predict(s_plus_one)
#This is for debug only
old_weights = model.weights.copy()
# This is gradient step similar to below
#V[state] = V[state] + alpha *(reward + gamma*V[s_plus_one] - V[state])
model.weights += alpha * (
target - model.predict(state)) * model.gradient(state)
biggest_change = max(biggest_change,
np.abs(old_weights - model.weights).sum())
deltas.append(biggest_change)
# We need to recreate the V
V = {}
for s in states:
print(s)
if s in game.actions:
V[s] = model.predict(s)
else:
# terminal state or state we can't otherwise get to
V[s] = 0
print("Policy:")
print_policy_beautifly(policy, game)
print("Values:")
print_values(V, game)
plt.plot(deltas)
plt.show()
def testForRandomPolicyEvaluationNegativeGrid():
print("Negative Grid Random Policy Testing!")
gamma = 0.7
g = NegativeGrid(-0.50)
p = RandomPolicy.createRandomPolicy(g.actions, g.width, g.height)
print("Before itterative improvement:")
print("========================================")
print_policy_beautifly(p, g)
print_policy(p, g)
for i in range(0, 1000):
v = evaluateValueFunction(g, p, gamma=gamma)
p = itteratePolicy(g, v, p, gamma=gamma)
print("After improvement:")
print_policy_beautifly(p, g)
print_policy(p, g)
print_values(v, g)
def testForDetermenisticPolicyEvaluation():
print("Standart Grid Deterministic Policy Testing!")
gamma = 0.5
g = StandardGrid()
p2 = RandomPolicy.createRandomDeterministicPolicy(g.actions, g.width,
g.height)
print("Before itterative improvement:")
print("========================================")
print_policy_beautifly(p2, g)
print_policy(p2, g)
print("========================================")
for i in range(0, 100):
V2 = evaluateValueFunction(g, p2, gamma=gamma)
p2 = itterateDetermenisticPolicy(g, V2, p2, gamma=gamma)
print("After improvement:")
print_policy_beautifly(p2, g)
print_policy(p2, g)
print_values(V2, g)
def testToEvaluateValue():
gamma = 1
g = StandardGrid()
p1 = generateRandomPolicy()
V1 = evaluateValueFunction(g, p1, gamma)
print("========================================")
print_policy(p1, g)
print_policy_beautifly(p1, g)
print("========================================")
print_values(V1, g)
#####
g = StandardGrid()
p2 = generateDeterministicPolicyTwo()
V2 = evaluateValueFunction(g, p2, gamma)
print("========================================")
print_policy(p2, g)
print_policy_beautifly(p2, g)
print("========================================")
print_values(V2, g)
def test_td0_sarsa(alpha=0.1, gamma=0.9, eps=0.1):
game = NegativeGrid()
print_values(game.rewards, game)
allActions = (GridWorldMove.up, GridWorldMove.down, GridWorldMove.right,
GridWorldMove.left)
states = game.allStates()
Q = {}
Q_visists = {}
Q_visists_debug = {}
for s in states:
Q[s] = {}
Q_visists[s] = {}
Q_visists_debug[s] = 0
for a in allActions:
Q[s][a] = 0
Q_visists[s][a] = 1.0
t = 1.0
deltas = []
for j in xrange(100000):
## Learning rate change
if j % 100 == 0:
t += 10e-3
if j % 1000 == 0:
print(".")
max_change = 0
state = game.start
game.position = state
game_over = False
action, _ = max_dict(Q[s])
action = eps_random_action(action, eps=0.5 / t)
while not game_over:
r = game.move(action)
state2 = game.position
game_over = game.gameOver()
## Let's find possible next actions
action2, _ = max_dict(Q[state2])
action2 = eps_random_action(action2, eps=0.5 / t) # epsilon-greedy
## At this stage we have all SARSA, let's calculate Q
betta = alpha / Q_visists[state][action]
Q_visists[state][action] += 0.001 ## 0.005
old_qsa = Q[state][action]
Q[state][action] = Q[state][action] + betta * (
r + gamma * Q[state2][action2] - Q[state][action])
max_change = max(max_change, abs(Q[state][action] - old_qsa))
Q_visists_debug[state] = Q_visists_debug.get(state, 0) + 1
action = action2
state = state2
deltas.append(max_change)
plt.plot(deltas)
plt.show()
policy = {}
V = {}
for s in game.actions.keys():
a, max_q = max_dict(Q[s])
policy[s] = [0, 0, 0, 0]
policy[s][a] = 1.0
V[s] = max_q
print("Policy:")
print_policy_beautifly(policy, game)
print("Values:")
print_values(V, game)
def test_td0_q_learning(itterations=1000,
alpha=0.1,
gamma=0.9,
eps=0.5,
delta_t=10e-3,
learning_decay=0.0001):
game = NegativeGrid(-0.2) #StandardGrid()
print_values(game.rewards, game)
allActions = (GridWorldMove.up, GridWorldMove.down, GridWorldMove.right,
GridWorldMove.left)
states = game.allStates()
Q = {}
Q_visists = {}
Q_visists_debug = {}
for s in states:
Q[s] = {}
Q_visists[s] = {}
Q_visists_debug[s] = 0
for a in allActions:
Q[s][a] = 0
Q_visists[s][a] = 1.0
t = 1.0
deltas = []
for j in xrange(itterations):
## Learning rate change
if j % 100 == 0:
t += delta_t
if j % 1000 == 0:
print(".")
max_change = 0
state = game.start
game.position = state
game_over = False
action = max_dict(Q[s])[0]
while not game_over:
## The fact that we use e-greedy makes or actions non optimal during the play
## However this allows us to be off-policy
## That makes Q-LEarning - an OFF-Policy algorithm
action = eps_random_action(action, eps=eps / t)
r = game.move(action)
state2 = game.position
game_over = game.gameOver()
## Let's find possible next actions
action2, q_max_a2 = max_dict(Q[state2])
## At this stage we have all Q-Learning params
betta = alpha / Q_visists[state][action]
Q_visists[state][action] += learning_decay
old_qsa = Q[state][action]
### So we are taking Q* here despite not taking the best move as a future move.
Q[state][action] = Q[state][action] + betta * (
r + gamma * q_max_a2 - Q[state][action])
max_change = max(max_change, abs(Q[state][action] - old_qsa))
Q_visists_debug[state] = Q_visists_debug.get(state, 0) + 1
action = action2
state = state2
deltas.append(max_change)
plt.plot(deltas)
plt.show()
policy = {}
V = {}
for s in game.actions.keys():
a, max_q = max_dict(Q[s])
policy[s] = [0, 0, 0, 0]
policy[s][a] = 1.0
V[s] = max_q
print("Policy:")
print_policy_beautifly(policy, game)
print("Values:")
print_values(V, game)
def test_td0_sarsa_approximated(alpha=0.1,
gamma=0.9,
eps=0.1,
itterations=50000):
game = StandardGrid() #NegativeGrid(-0.1)
print_values(game.rewards, game)
allActions = (GridWorldMove.up, GridWorldMove.down, GridWorldMove.right,
GridWorldMove.left)
states = game.allStates()
t = 1.0
deltas = []
model = LinearSarsaState()
for j in xrange(itterations):
## Learning rate change
if j % 1000 == 0:
t += 10e-3
if j % 1000 == 0:
print(".")
max_change = 0
state = game.start
game.position = state
game_over = False
Qs = model.getQs(state)
action = max_dict(Qs)[0]
action = eps_random_action(action, eps=0.5 / t)
alpha = alpha / 1.00005
while not game_over:
r = game.move(action)
state2 = game.position
game_over = game.gameOver()
Qs2 = model.getQs(state2)
## Let's find possible next actions
action2 = max_dict(Qs2)[0]
action2 = eps_random_action(action2, eps=0.5 / t) # epsilon-greedy
## At this stage we have all SARSA, let's calculate Q
old_weights = model.weights.copy()
model.weights += alpha * (
r + gamma * model.predict(state2, action2) -
model.predict(state, action)) * model.gradient(state, action)
max_change = max(max_change,
np.abs(model.weights - old_weights).sum())
action = action2
state = state2
deltas.append(max_change)
policy = {}
V = {}
Q = {}
for s in game.actions.keys():
Qs = model.getQs(s)
Q[s] = Qs
a, max_q = max_dict(Qs)
policy[s] = [0, 0, 0, 0]
policy[s][a] = 1
V[s] = max_q
print("Policy:")
print_policy_beautifly(policy, game)
print("Values:")
print_values(V, game)
plt.plot(deltas)
plt.show()
