def main(
model_h5_file: str,
target_h5_file: str,
two_tile_prob: float,
random_seed: int,
# num_games: int,
# val_split: float,
train_game_dir: str,
# val_game_dir: str,
):
"""
Build training/validation dataset by playing N games.
model_h5_file - path to a model saved in .h5 file to use when selecting
actions (via epsilon-greedy method), etc.
target_h5_file - path to a model saved in .h5 file to use when computing
labels for experience tuples
"""
# build training dataset by playing N complete games
data_train = []
labels_train = []
data_val = []
labels_val = []
TWO_TILE_PROB = two_tile_prob
RANDOM_SEED = random_seed
# NUM_GAMES = num_games
# VAL_SPLIT = val_split
# NUM_TRAIN_GAMES = int(NUM_GAMES * (1 - VAL_SPLIT))
TRAIN_GAME_FILES_DIR = train_game_dir
# VAL_GAME_FILES_DIR = val_game_dir
print(f"==== Loading model from {model_h5_file} ====")
value_model = load_model(model_h5_file)
print(f"==== Loading target model from {target_h5_file} ====")
target_model = load_model(target_h5_file)
# Weights & Biases
wandb.init(project="2048-deep-rl")
# for epsilon-greedy action selection
# set initial epsilon to 1, and then linearly anneal to a lower value
epsilon_start = 1.0
epsilon_final = 0.05
epsilon_anneal_start_t = 1
epsilon_anneal_end_t = 50_000
# discount factor
gamma = 0.99
# hold the target Q-model fixed for this many timesteps before updating with minibatch
target_update_delay = 10_000
# save value model
value_model_save_period = 1_000
# Each SGD update is calculated over this many experience tuples (sampled randomly from the replay memory)
minibatch_size = 32
# SGD updates are sampled from this number of the most recent experience tuples
replay_memory_capacity = 10_000
# how many timesteps of learning per episode
timesteps_per_episode = 100_000
# populate replay memory by using a uniform random policy for this many timesteps before learning starts
burnin_period = 10_000
# action is encoded as an int in 0..3
# TODO refactor this to be a global defined in a different file, maybe experience_replay_utils.py?
ACTIONS = ["Up", "Down", "Left", "Right"]
value_model.compile(optimizer="sgd", loss="mean_squared_error")
# Generate the experience tuples to fill replay memory for one episode of training
#
# replay memory format:
# - state,
# - action,
# - reward,
# - max Q-value of successor state based on target network,
# - Q(state,action) based on current network
# TODO abstract away the generation of experience tuples into a generator class?
replay_memory_ndarray = np.zeros((replay_memory_capacity, 2 * 16 * 17 + 2))
replay_memory_idx = 0
game = Game()
game.new_game(random_seed=RANDOM_SEED, game_dir=TRAIN_GAME_FILES_DIR)
np.random.seed(RANDOM_SEED)
print(f"New game (random seed = {RANDOM_SEED})")
for t in range(burnin_period):
if game.state.game_over:
RANDOM_SEED = random.randrange(100_000)
game = Game()
game.new_game(random_seed=RANDOM_SEED,
game_dir=TRAIN_GAME_FILES_DIR)
np.random.seed(RANDOM_SEED)
print(f"New game (random seed = {RANDOM_SEED})")
current_state = game.state.copy()
# print("current state:", current_state.tiles)
# choose an action uniformly at random during the burn-in period (to initially populate the replay memory)
action = np.random.choice(np.arange(4))
# update current state using the chosen action
game.move(ACTIONS[action])
new_state = game.state.copy()
# print("new state:", new_state.tiles)
reward = new_state.score - current_state.score
# save the (s,a,s',r) experience tuple (flattened) to replay memory
exp = ExperienceReplay(current_state.tiles, action, new_state.tiles,
reward)
replay_memory_ndarray[replay_memory_idx] = exp.flatten()
replay_memory_idx = replay_memory_idx + 1
if replay_memory_idx == replay_memory_capacity:
replay_memory_idx = 0
# if reward > 0:
# print(f"experience tuple with reward: {exp}")
# replay_memory_ndarray = np.asarray(replay_memory)
print("replay_memory shape:", replay_memory_ndarray.shape)
# assert len(replay_memory) == burnin_period
# print("writing replay memory to file:")
# with open("replay_memory_burnin.txt", "w") as burn_in_file:
# for i in range(len(replay_memory)):
# exp = ExperienceReplay.from_flattened(replay_memory[i])
# burn_in_file.write(repr(exp) + "\n\n")
timesteps_since_last_update = 0
last_time_check = time.perf_counter()
for t in range(timesteps_per_episode):
epsilon = linear_anneal_parameter(
epsilon_start,
epsilon_final,
epsilon_anneal_start_t,
epsilon_anneal_end_t,
t,
)
fit_verbose = 0
if (t + 1) % 500 == 0:
print(f"timestep = {t}, epsilon = {epsilon}")
new_time = time.perf_counter()
print(f"avg time per step: {(new_time - last_time_check) / t}")
fit_verbose = 1
if game.state.game_over:
RANDOM_SEED = random.randrange(100_000)
game = Game()
game.new_game(random_seed=RANDOM_SEED,
game_dir=TRAIN_GAME_FILES_DIR)
np.random.seed(RANDOM_SEED)
# print(f"New game (random seed = {RANDOM_SEED})")
current_state = game.state.copy()
# print("current state:", current_state.tiles)
# choose an action (epsilon-greedy)
epsilon_greedy_roll = np.random.random_sample()
if epsilon_greedy_roll < epsilon:
action = np.random.choice(np.arange(4))
# print("chosen action (randomly):", ACTIONS[action])
else:
# choose the "best" action based on current model weights -> Q values
network_input = np.expand_dims(convert_tiles_to_bitarray(
current_state.tiles),
axis=0)
network_output = value_model.predict(network_input)[0]
assert len(network_output) == 4
# print(f"network output: {network_output}")
action = np.argmax(network_output)
# print("chosen action (best):", ACTIONS[action])
# update current state using the chosen action
game.move(ACTIONS[action])
new_state = game.state.copy()
# print("new state:", new_state.tiles)
reward = new_state.score - current_state.score
# save the (s,a,s',r) experience tuple (flattened) to replay memory
exp = ExperienceReplay(current_state.tiles, action, new_state.tiles,
reward)
replay_memory_ndarray[replay_memory_idx] = exp.flatten()
replay_memory_idx = replay_memory_idx + 1
# if reward > 0:
# print(f"experience tuple with reward: {exp}")
# Constrain replay memory capacity
if replay_memory_idx == replay_memory_capacity:
replay_memory_idx = 0
# if len(replay_memory) > replay_memory_capacity:
# shift = len(replay_memory) - replay_memory_capacity
# replay_memory = replay_memory[shift:]
# assert len(replay_memory) <= replay_memory_capacity
# Sample a minibatch of experience tuples from replay memory
# replay_memory_ndarray = np.asarray(replay_memory)
# TODO is the minibatch sampled without replacement?
minibatch_indices = np.random.choice(replay_memory_ndarray.shape[0],
minibatch_size,
replace=False)
minibatch = replay_memory_ndarray[minibatch_indices]
# print(f"minibatch shape: ", minibatch.shape)
assert minibatch.shape == (minibatch_size,
replay_memory_ndarray.shape[1])
# Compute the labels for the minibatch based on target Q model (vectorized)
minibatch_succs = replay_memory_ndarray[minibatch_indices,
(16 * 17 + 1):(2 * 16 * 17 +
1)]
minibatch_rewards = replay_memory_ndarray[minibatch_indices,
(2 * 16 * 17 + 1)]
target_output = target_model.predict(minibatch_succs)
best_q_values = np.max(target_output, axis=1)
labels = minibatch_rewards + gamma * best_q_values
# # Compute the labels for the minibatch based on target Q model
# labels = np.zeros((minibatch_size,))
# # print(f"labels shape: ", labels.shape)
# for j in range(minibatch_size):
# # Parse out (s, a, s', r) from the experience tuple
# # minibatch_exp = ExperienceReplay.from_flattened(minibatch[j])
# successor_bitarray = minibatch[j, (16 * 17 + 1) : (2 * 16 * 17 + 1)]
# reward = minibatch[j, (2 * 16 * 17 + 1)]
# target_input = np.expand_dims(successor_bitarray, axis=0)
# target_output = target_model.predict(target_input)[0]
# best_q_value = np.max(target_output)
# # TODO check if the successor state is a terminal state: if so, then the label is just the reward
# labels[j] = reward + gamma * best_q_value
# # print(f"labels: ", labels)
# Perform SGD update on current Q model weights based on minibatch & labels
minibatch_x = minibatch[:, :(16 * 17)]
_first_record = minibatch_x[0].reshape((4, 4, 17))
# print(f"minibatch_x shape = {minibatch_x.shape}, first record = {_first_record}")
value_model.fit(x=minibatch_x,
y=labels,
batch_size=minibatch_size,
verbose=fit_verbose)
model_h5_filename = os.path.splitext(model_h5_file)[0]
model_h5_out = f"{model_h5_filename}_{t}.h5"
if t % value_model_save_period == 0 and t > 0:
print(f"==== Saving value model to {model_h5_out} ====")
value_model.save(model_h5_out)
# Only update the target model to match the current Q model every C timesteps
timesteps_since_last_update += 1
if timesteps_since_last_update >= target_update_delay:
timesteps_since_last_update = 0
# update the target model
model_h5_out = f"{model_h5_filename}_{t}.h5"
value_model.save(model_h5_out)
target_model = load_model(model_h5_out)
target_h5_filename = os.path.splitext(target_h5_file)[0]
target_h5_out = f"{target_h5_filename}_{t}.h5"
print(f"==== Saving target model to {target_h5_out} ====")
target_model.save(target_h5_out)
class GameWindow(tk.Frame):
def __init__(self, master=None):
super().__init__(master)
self.master = master
self.grid()
self.create_widgets()
def create_widgets(self):
self.game = Game()
self.game_tiles = GameTiles(master=self)
self.draw_game_tiles()
self.game_tiles.grid()
self.score_strvar = tk.StringVar()
self.game_score = tk.Label(self, textvariable=self.score_strvar)
self.draw_score()
self.game_score.grid()
self.new_game_button = tk.Button(self,
text="New Game",
command=self.new_game)
self.new_game_button.grid()
self.load_game_button = tk.Button(self,
text="Load Game",
command=self.load_game)
self.load_game_button.grid()
self.quit = tk.Button(self,
text="Quit",
fg="red",
command=self.master.destroy)
self.quit.grid()
def draw_game_tiles(self):
self.game_tiles.draw_tiles(self.game)
def draw_score(self):
self.score_strvar.set(f"Score: {self.game.state.score}")
def new_game(self):
self.master.bind('<Up>', self.move)
self.master.bind('<Down>', self.move)
self.master.bind('<Left>', self.move)
self.master.bind('<Right>', self.move)
self.game.new_game()
self.draw_game_tiles()
self.draw_score()
def load_game(self):
fname = filedialog.askopenfilename(filetypes=(("CSV files",
"*.csv"), ))
if fname:
# TODO add instructions to the GUI?
self.master.unbind('<Up>')
self.master.unbind('<Down>')
self.master.bind('<Left>', self.decrement_turn_number)
self.master.bind('<Right>', self.increment_turn_number)
print(f"Loading game from {fname}!")
with open(fname, 'r') as game_file:
self.game_states = game_file.readlines()
self.turn_number = 0
self.load_game_state()
def load_game_state(self):
print(f"turn number = {self.turn_number}")
self.game.state = GameState.from_csv_line(
self.game_states[self.turn_number])
next_action = self.game_states[self.turn_number].split(',')[-1].strip()
print(f"action from this state: {next_action}")
self.draw_game_tiles()
self.draw_score()
def increment_turn_number(self, event):
if self.turn_number < len(self.game_states) - 1:
self.turn_number += 1
self.load_game_state()
def decrement_turn_number(self, event):
if self.turn_number > 0:
self.turn_number -= 1
self.load_game_state()
def move(self, event):
print(f"moving in dir {event.keysym}")
self.game.move(event.keysym)
self.draw_game_tiles()
self.draw_score()
if self.game.state.game_over:
# TODO display a message on GUI
print("Game over! no moves available")
