def play(strategy, verbose=False, allow_unavailable_action=True):
"""Plays a single game, using a provided strategy.
Args:
strategy: A function that takes as argument a state and a list of available
actions and returns an action from the list.
allow_unavailable_action: Boolean, whether strategy is passed all actions
or just the available ones.
verbose: If true, prints game states, actions and scores.
Returns:
score, experiences where score is the final score and experiences is the
list Experience instances that represent the collected experience.
"""
game = Game()
state = game.state().copy()
game_over = game.game_over()
experiences = []
while not game_over:
if verbose:
print("Score:", game.score())
game.print_state()
old_state = state
next_action = strategy(
old_state,
range(4) if allow_unavailable_action else game.available_actions())
if game.is_action_available(next_action):
reward = game.do_action(next_action)
state = game.state().copy()
game_over = game.game_over()
if verbose:
print("Action:", ACTION_NAMES[next_action])
print("Reward:", reward)
experiences.append(
Experience(old_state, next_action, reward, state, game_over,
False, game.available_actions()))
else:
experiences.append(
Experience(state, next_action, 0, state, False, True,
game.available_actions()))
if verbose:
print("Score:", game.score())
game.print_state()
print("Game over.")
return game.score(), experiences
def test_do_action(choice):
choice.side_effect = [0, # First position
1] # First tile
state = np.array([[1, 2, 3, 3],
[5, 6, 7, 8],
[5, 2, 7, 0],
[1, 0, 3, 0]])
game = Game(state=state)
game.do_action(3) # DOWN
new_state = np.array([[1, 0, 0, 0],
[1, 2, 3, 0],
[6, 6, 8, 3],
[1, 2, 3, 8]])
game.print_state()
assert (game.state() == new_state).all()
# Score is 2 ** 6 + 2 ** 8
assert game.score() == 320
def test_do_action(choice):
choice.side_effect = [
0, # First position
1
] # First tile
state = np.array([[[1, 2, 3], [5, 6, 7], [5, 2, 7]],
[[1, 2, 3], [5, 6, 7], [5, 2, 7]],
[[1, 2, 3], [5, 6, 7], [5, 2, 7]]])
game = Game(state=state)
game.do_action(3) # DOWN
new_state = np.array([[[0, 2, 0], [1, 6, 3], [6, 2, 8]],
[[0, 2, 0], [1, 6, 3], [6, 2, 8]],
[[0, 2, 0], [1, 6, 3], [6, 2, 8]]])
game.print_state()
assert (game.state() == new_state).all()
# Score is (2 ** 6 + 2 ** 8)*3
assert game.score() == 960
def test_do_action(choice):
choice.side_effect = [0, # First position
1] # First tile
state = np.array([[1, 2, 3, 3],
[5, 6, 7, 8],
[5, 2, 7, 0],
[1, 0, 3, 0]])
game = Game(state=state)
game.do_action(3) # DOWN
new_state = np.array([[1, 0, 0, 0],
[1, 2, 3, 0],
[6, 6, 8, 3],
[1, 2, 3, 8]])
game.print_state()
assert (game.state() == new_state).all()
# Score is 2 ** 6 + 2 ** 8
assert game.score() == 320
def play(strategy, verbose=False, allow_unavailable_action=True):
"""Plays a single game, using a provided strategy.
Args:
strategy: A function that takes as argument a state and a list of available
actions and returns an action from the list.
allow_unavailable_action: Boolean, whether strategy is passed all actions
or just the available ones.
verbose: If true, prints game states, actions and scores.
Returns:
score, experiences where score is the final score and experiences is the
list Experience instances that represent the collected experience.
"""
game = Game()
state = game.state().copy()
game_over = game.game_over()
experiences = []
while not game_over:
if verbose:
print("Score:", game.score())
game.print_state()
old_state = state
next_action = strategy(
old_state, range(4) if allow_unavailable_action
else game.available_actions())
if game.is_action_available(next_action):
reward = game.do_action(next_action)
state = game.state().copy()
game_over = game.game_over()
if verbose:
print("Action:", ACTION_NAMES[next_action])
print("Reward:", reward)
experiences.append(Experience(old_state, next_action, reward, state,
game_over, False, game.available_actions()))
else:
experiences.append(Experience(state, next_action, 0, state, False, True,
game.available_actions()))
if verbose:
print("Score:", game.score())
game.print_state()
print("Game over.")
return game.score(), experiences
action_size = env.action_space.n #in our case 4
agent = DQNAgent(state_size, action_size)
# agent.load("./save/cartpole-dqn.h5")
done = False
batch_size = 32
for e in range(EPISODES):
state = game.state()
count += 1
#state = env.reset()
#state = np.reshape(state, [1, state_size])
while not game.game_over():
# env.render()
action = randint(0, 3) #replace with epsilon greedy strategy
#action = agent.act(state)
reward = game.do_action(action)
next_state = game.state()
#next_state, reward, done, _ = env.step(action)
#reward = reward if not done else -10
#next_state = np.reshape(next_state, [1, state_size])
agent.remember(state, action, reward, next_state, done)
state = next_state
if done:
print("episode: {}/{}, score: {}, e: {:.2}".format(
e, EPISODES, agent.epsilon))
break
print("bla")
print("runs: ")
if len(agent.memory) > batch_size:
agent.replay(batch_size)
# if e % 10 == 0:
