def __init__(self, m, n, hot_value=-1, cold_value=1):
self.m = int(m)
self.n = int(m)
self.hot_value = float(hot_value)
self.cold_value = float(cold_value)
self.board = create_cold_board(
self.m, self.n, cold_value=cold_value, default=hot_value)
def wythoff_dqn1(epsilon=0.1,
gamma=0.8,
learning_rate=0.1,
num_episodes=10,
batch_size=100,
memory_capacity=10000,
game='Wythoff10x10',
network='DQN',
anneal=False,
tensorboard=None,
update_every=5,
self_play=False,
save=False,
save_model=False,
monitor=None,
return_none=False,
debug=False,
progress=False,
seed=None):
"""Learn to play Wythoff's w/ e-greedy random exploration.
Note: Learning is based on a player-opponent joint action formalism
and tabular Q-learning.
"""
# ------------------------------------------------------------------------
# Init
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Logs...
if tensorboard is not None:
try:
os.makedirs(tensorboard)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
writer = SummaryWriter(log_dir=tensorboard)
if monitor is not None:
monitored = create_monitored(monitor)
# Env...
if tensorboard is not None:
env = create_env(game, monitor=True)
else:
env = create_env(game, monitor=False)
env.seed(seed)
np.random.seed(seed)
# ------------------------------------------------------------------------
# Init
#
# Scores
score = 0
total_reward = 0
# Agents, etc
m, n, board, available = peek(env)
all_possible_moves = create_all_possible_moves(m, n)
if network == 'DQN':
player = DQN(m, n, num_actions=len(all_possible_moves))
opponent = DQN(m, n, num_actions=len(all_possible_moves))
elif network == 'DQN_mlp':
player = DQN_mlp(m, n, num_actions=len(all_possible_moves))
opponent = DQN_mlp(m, n, num_actions=len(all_possible_moves))
else:
raise ValueError("network must DQN or DQN_mlp")
if debug:
print(f"---------------------------------------")
print("Setting up....")
print(f">>> Network is {player}")
print(f">>> Memory capacity {memory_capacity} ({batch_size})")
player_memory = ReplayMemory(memory_capacity)
opponent_memory = ReplayMemory(memory_capacity)
if self_play:
player_memory = opponent_memory
player_optimizer = optim.Adam(player.parameters(), learning_rate)
opponent_optimizer = optim.Adam(opponent.parameters(), learning_rate)
moves = MoveCount(m, n)
# ------------------------------------------------------------------------
for episode in range(1, num_episodes + 1):
# Re-init
#
# Scores
steps = 1
done = False
mover = 'opponent' # This will shift to player on the first move.
transitions = []
# Worlds
state = env.reset()
x, y, board, available = state
board = tuple(flatten_board(board))
moves.update((x, y))
if debug:
print(f"---------------------------------------")
print(f">>> NEW GAME ({episode}).")
print(f">>> Initial position ({x}, {y})")
print(f">>> Initial moves {available}")
print(f">>> Cold available {locate_cold_moves(x, y, available)}")
print(f">>> All cold {locate_all_cold_moves(x, y)}")
# Anneal epsilon?
if anneal:
epsilon_e = epsilon * (1.0 / np.log((episode + np.e)))
else:
epsilon_e = epsilon
# -------------------------------------------------------------------
# Play a game
while not done:
# Choose a mover
mover = shift_mover(mover)
memory = shift_memory(mover, player_memory, opponent_memory)
model = shift_model(mover, player, opponent)
# Convert board to a model(state)
state_hat = torch.from_numpy(np.array(board).reshape(m, n))
state_hat = state_hat.unsqueeze(0).unsqueeze(1).float()
# Get and filter Qs
Qs = model(state_hat).float().detach() # torch
Qs = Qs.numpy().squeeze()
mask = build_mask(available, m, n).flatten()
Qs *= mask
# Choose a move
index = np.nonzero(mask)[0].tolist()
move_i = e_greedy(Qs, epsilon=epsilon_e, index=index, mode='numpy')
# Re-index move_i to match 'available' index
move_a = index.index(move_i)
move = available[move_a]
# Analyze it...
if move in locate_cold_moves(x, y, available):
score += (1 - score) / episode
# Play it
state_next, reward, done, _ = env.step(move)
(x_next, y_next, board_next, available_next) = state_next
total_reward += reward
# Save transitions, as tensors to be used at training time
moves.update(move)
state_hat_next = torch.from_numpy(
np.array(board_next).reshape(m, n))
state_hat_next = state_hat_next.unsqueeze(0).unsqueeze(1).float()
transitions.append([
state_hat.float(),
torch.from_numpy(mask),
torch.tensor(move_i),
state_hat_next.float(),
torch.tensor([reward]).unsqueeze(0).float()
])
# Shift states
state = deepcopy(state_next)
board = deepcopy(board_next)
available = deepcopy(available_next)
x = deepcopy(x_next)
y = deepcopy(y_next)
steps += 1
# -
if debug:
print(f">>> {mover}: {move}")
print(f">>> new position: ({x_next}, {y_next})")
# ----------------------------------------------------------------
# Learn from the game
#
# Find the losers transition and update its reward w/ -reward
if steps > 2:
transitions[-2][4] = transitions[-1][4] * -1
# Update the memories using the transitions from this game
for i in range(0, len(transitions), 2):
s, x, a, sn, r = transitions[i]
player_memory.push(s.to(device), x.to(device), a.to(device),
sn.to(device), r.to(device))
for i in range(1, len(transitions), 2):
s, x, a, sn, r = transitions[i]
opponent_memory.push(s.to(device), x.to(device), a.to(device),
sn.to(device), r.to(device))
# Bypass is we don't have enough in memory to learn
if episode < batch_size:
continue
# Learn, samping batches of transitions from memory
player, player_loss = train_dqn(batch_size,
player,
player_memory,
player_optimizer,
device,
gamma=gamma)
opponent, opponent_loss = train_dqn(batch_size,
opponent,
opponent_memory,
opponent_optimizer,
device,
gamma=gamma)
# ----------------------------------------------------------------
# Logs...
if progress:
print(f"---")
if progress or debug:
print(f">>> episode: {episode}")
print(f">>> winner: {mover}")
if debug or progress:
print(f">>> Q: {Qs}")
print(f">>> max(Q): {Qs.max()}")
print(f">>> min(Q): {Qs.min()}")
print(f">>> stdev(Q): {Qs.std()}")
print(
f">>> loss (player: {player_loss}, opponent: {opponent_loss})")
print(f">>> player score: {score}")
print(f">>> epsilon: {epsilon_e}")
if tensorboard and (int(episode) % update_every) == 0:
writer.add_scalar('reward', reward, episode)
writer.add_scalar('epsilon_e', epsilon_e, episode)
writer.add_scalar('player_loss', player_loss, episode)
writer.add_scalar('opponent_loss', opponent_loss, episode)
writer.add_scalar('steps', steps, episode)
writer.add_scalar('score', score, episode)
# Cold ref:
cold = create_cold_board(m, n)
plot_wythoff_board(cold,
vmin=0,
vmax=1,
path=tensorboard,
name='cold_board.png')
writer.add_image('cold_positions',
torch.from_numpy(
skimage.io.imread(
os.path.join(tensorboard,
'cold_board.png'))),
0,
dataformats='HWC')
# Extract all value boards, and find extrema
values = torch.zeros((len(all_possible_moves), m, n))
for i, a in enumerate(all_possible_moves):
example = create_board(a[0], a[1], m, n)
values[i, :, :] = player(state_hat).detach().float().reshape(
m, n)
mean_values = torch.mean(values, 0)
# max_values, _ = torch.max(values, 0)
# min_values, _ = torch.min(values, 0)
# Log
writer.add_scalar('Q_mean', torch.mean(mean_values), episode)
# Plot mean
plot_wythoff_board(mean_values.numpy(),
vmin=mean_values.numpy().min(),
vmax=mean_values.numpy().max(),
path=tensorboard,
name='player_mean_values.png')
writer.add_image('mean player',
torch.from_numpy(
skimage.io.imread(
os.path.join(tensorboard,
'player_mean_values.png'))),
0,
dataformats='HWC')
# Plot move count
plot_wythoff_board(moves.count,
vmax=moves.count.max() / 10,
vmin=0,
path=tensorboard,
name='moves.png')
writer.add_image('moves',
torch.from_numpy(
skimage.io.imread(
os.path.join(tensorboard, 'moves.png'))),
0,
dataformats='HWC')
if monitor and (int(episode) % update_every) == 0:
all_variables = locals()
for k in monitor:
monitored[k].append(float(all_variables[k]))
# --------------------------------------------------------------------
if save_model:
state = {
'stumbler_player_dict': player,
'stumbler_opponent_dict': opponent
}
torch.save(state, save + ".pytorch")
if monitor:
save_monitored(save, monitored)
if tensorboard:
writer.close()
result = (player, opponent), (score / episode, total_reward)
if return_none:
result = None
return result
def wythoff_dqn2(epsilon=0.1,
gamma=0.5,
learning_rate=1e-6,
num_episodes=100,
batch_size=20,
memory_capacity=100,
game='Wythoff10x10',
network='DQN_xy1',
anneal=False,
tensorboard=None,
update_every=5,
double=False,
double_update=10,
save=False,
save_model=False,
monitor=None,
return_none=False,
debug=False,
device='cpu',
clip_grad=False,
progress=False,
zero=False,
seed=None):
"""Learning Wythoff's, with a DQN."""
# ------------------------------------------------------------------------
# Init
num_episodes = int(num_episodes)
batch_size = int(batch_size)
memory_capacity = int(memory_capacity)
update_every = int(update_every)
# Logs...
if tensorboard is not None:
try:
os.makedirs(tensorboard)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
writer = SummaryWriter(log_dir=tensorboard)
if monitor is not None:
monitored = create_monitored(monitor)
# Env...
if tensorboard is not None:
env = create_env(game, monitor=True)
else:
env = create_env(game, monitor=False)
env.seed(seed)
np.random.seed(seed)
# ------------------------------------------------------------------------
# Init
#
# Scores
score = 0
total_reward = 0
# Agents, etc
m, n, board, available = peek(env)
all_possible_moves = create_all_possible_moves(m, n)
# Is network a nn.Module?
if hasattr(network, "forward"):
Model = network
# Is it the name of a azad model?
else:
Model = getattr(azad.models, network)
player = Model().to(device)
target = Model().to(device)
if double:
target.load_state_dict(player.state_dict())
target.eval()
else:
target = None
if debug:
print(f"---------------------------------------")
print("Setting up....")
print(f">>> Device: {device}")
print(f">>> Network is {player}")
print(f">>> Memory capacity {memory_capacity} ({batch_size})")
memory = ReplayMemory(memory_capacity)
# optimizer = optim.Adam(player.parameters(), learning_rate)
optimizer = optim.SGD(player.parameters(), learning_rate)
moves = MoveCount(m, n)
opts = OptimalCount(0)
# ------------------------------------------------------------------------
for episode in range(1, num_episodes + 1):
# Re-init
transitions = []
state = env.reset()
x, y, board, available = state
moves.update((x, y))
if debug:
print(f"---------------------------------------")
print(f">>> NEW GAME ({episode}).")
print(f">>> Initial position ({x}, {y})")
print(f">>> Initial moves {available}")
print(f">>> Cold available {locate_cold_moves(x, y, available)}")
print(f">>> All cold {locate_all_cold_moves(x, y)}")
# Anneal epsilon?
if anneal:
epsilon_e = epsilon * (1.0 / np.log((episode + np.e)))
else:
epsilon_e = epsilon
# -------------------------------------------------------------------
# Play a game
steps = 1
done = False
while not done:
# Choose a move
Qs = build_Qs(player,
state,
available,
device=device,
mode="numpy")
move_i = e_greedy(Qs, epsilon=epsilon_e, mode='numpy')
move = available[move_i]
moves.update(move)
# Analyze it...
best = 0.0
if cold_move_available(x, y, available):
if move in locate_cold_moves(x, y, available):
best = 1.0
score += (best - score) / (episode + 1)
# Play it
state_next, reward, done, _ = env.step(move)
(x_next, y_next, board_next, available_next) = state_next
# Track value statistics
total_reward += reward
Q = Qs[move_i]
prediction_error = Qs.max() - Q
advantage = Q - Qs[np.nonzero(Qs)].mean()
# Save transitions, as tensors to be used at training time
# (onto GPU)
transitions.append([
# S
torch.tensor((x, y)).unsqueeze(0).unsqueeze(1).float(),
# A
torch.tensor(move).unsqueeze(0),
# S'
torch.tensor(
(x_next, y_next)).unsqueeze(0).unsqueeze(1).float(),
# R
torch.tensor([reward]).unsqueeze(0).float(),
])
# -
if debug:
print(f">>> position: {(x, y)}")
print(f">>> num available: {len(available)}")
print(f">>> available: {available}")
print(f">>> Qs (filtered): {Qs}")
print(f">>> new position: ({x_next}, {y_next})")
# Shift states
state = deepcopy(state_next)
board = deepcopy(board_next)
available = deepcopy(available_next)
x = deepcopy(x_next)
y = deepcopy(y_next)
steps += 1
# ----------------------------------------------------------------
# Learn from the game
#
# Find the losers transition and update its reward w/ -reward
if steps > 2:
transitions[-2][3] = transitions[-1][3] * -1
# Update the memories using the transitions from this game
for i in range(0, len(transitions)):
memory.push(*transitions[i])
if debug:
print(f">>> final transitions: {transitions[-2:]}")
# Bypass if we don't have enough in memory to learn
if episode < batch_size:
continue
# Learn, samping a batch of transitions from memory
player, loss = train_dqn(batch_size,
player,
memory,
optimizer,
device,
target=target,
gamma=gamma,
clip_grad=clip_grad)
# Update target net, if in double mode and time is right.
if double and (episode % double_update == 0):
target.load_state_dict(player.state_dict())
# ----------------------------------------------------------------
# Logs...
if progress:
print(f"---")
if progress or debug:
print(f">>> episode: {episode}")
if debug or progress:
print(f">>> loss {loss}")
print(f">>> Q(last,a): {Q}")
print(f">>> epsilon: {epsilon_e}")
print(f">>> score: {score}")
if tensorboard and (int(episode) % update_every) == 0:
writer.add_scalar('reward', reward, episode)
writer.add_scalar('epsilon_e', epsilon_e, episode)
writer.add_scalar('loss', loss, episode)
writer.add_scalar('steps', steps, episode)
writer.add_scalar('score', score, episode)
# Cold ref:
cold = create_cold_board(m, n)
plot_wythoff_board(cold,
vmin=0,
vmax=1,
path=tensorboard,
name='cold_board.png')
writer.add_image('cold_positions',
torch.from_numpy(
skimage.io.imread(
os.path.join(tensorboard,
'cold_board.png'))),
0,
dataformats='HWC')
# Extract all value boards, and find extrema
values = torch.zeros((len(all_possible_moves), m, n))
for i, a in enumerate(all_possible_moves):
sample_hat = np.asarray(create_board(a[0], a[1], m, n))
sample_hat = torch.from_numpy(sample_hat)
sample_hat = sample_hat.unsqueeze(0).unsqueeze(1).float()
values[i, :, :] = player(sample_hat).detach().float().reshape(
m, n)
mean_values = torch.mean(values, 0)
max_values, _ = torch.max(values, 0)
min_values, _ = torch.min(values, 0)
# Log
writer.add_scalar('Q_mean', torch.mean(mean_values), episode)
writer.add_scalar('Q_min', torch.mean(min_values), episode)
writer.add_scalar('Q_max', torch.mean(max_values), episode)
# Plot mean
plot_wythoff_board(mean_values.numpy(),
vmin=mean_values.numpy().min(),
vmax=mean_values.numpy().max(),
path=tensorboard,
name='player_mean_values.png')
writer.add_image('mean player',
torch.from_numpy(
skimage.io.imread(
os.path.join(tensorboard,
'player_mean_values.png'))),
0,
dataformats='HWC')
# Plot max
plot_wythoff_board(max_values.numpy(),
vmin=max_values.numpy().min(),
vmax=max_values.numpy().max(),
path=tensorboard,
name='player_max_values.png')
writer.add_image('max player',
torch.from_numpy(
skimage.io.imread(
os.path.join(tensorboard,
'player_max_values.png'))),
0,
dataformats='HWC')
# Plot min
plot_wythoff_board(min_values.numpy(),
vmin=min_values.numpy().min(),
vmax=min_values.numpy().max(),
path=tensorboard,
name='player_min_values.png')
writer.add_image('min player',
torch.from_numpy(
skimage.io.imread(
os.path.join(tensorboard,
'player_min_values.png'))),
0,
dataformats='HWC')
# Plot move count
plot_wythoff_board(moves.count,
vmax=moves.count.max() / 10,
vmin=0,
path=tensorboard,
name='moves.png')
writer.add_image('moves',
torch.from_numpy(
skimage.io.imread(
os.path.join(tensorboard, 'moves.png'))),
0,
dataformats='HWC')
if monitor and (int(episode) % update_every) == 0:
all_variables = locals()
for k in monitor:
monitored[k].append(float(all_variables[k]))
# --------------------------------------------------------------------
if monitor and save:
save_monitored(save, monitored)
if tensorboard:
writer.close()
result = {"player": player.state_dict(), "score": score}
if target is not None:
result['target'] = target.state_dict()
if save:
torch.save(result, save + ".pytorch")
if monitor and not save:
result["monitored"] = monitored
if return_none:
result = None
return result
def wythoff_oracular_strategy(num_episodes=1000,
learning_rate=0.025,
num_hidden1=100,
num_hidden2=25,
stumbler_game='Wythoff10x10',
strategist_game='Wythoff50x50',
tensorboard=None,
update_every=50,
save=None,
return_none=False,
debug=False,
seed=None):
"""Train a strategist layer on perfact data."""
# ------------------------------------------------------------------------
# Setup
if tensorboard is not None:
try:
os.makedirs(tensorboard)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
writer = SummaryWriter(log_dir=tensorboard)
# Boards, etc
m, n, board, _ = peek(create_env(strategist_game))
o, p, _, _ = peek(create_env(stumbler_game))
if debug:
print(">>> TRANING AN OPTIMAL STRATEGIST.")
print(">>> Train board {}".format(o, p))
print(">>> Test board {}".format(m, n))
# Seeding...
np.random.seed(seed)
# Train params
strategic_default_value = 0.0
batch_size = 64
# ------------------------------------------------------------------------
# Build a Strategist, its memory, and its optimizer
# Create a model, of the right size.
# model = HotCold2(2, num_hidden1=num_hidden1)
model = HotCold3(2, num_hidden1=num_hidden1, num_hidden2=num_hidden2)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
memory = ReplayMemory(10000)
# Run learning episodes. The 'stumbler' is just the opt
# cold board
for episode in range(num_episodes):
# The cold spots are '1' everythig else is '0'
strategic_value = create_cold_board(o, p)
# ...Into tuples
s_data = convert_ijv(strategic_value)
s_data = balance_ijv(s_data, strategic_default_value)
for d in s_data:
memory.push(*d)
loss = 0.0
if len(memory) > batch_size:
# Sample data....
coords = []
values = []
samples = memory.sample(batch_size)
for c, v in samples:
coords.append(c)
values.append(v)
coords = torch.tensor(
np.vstack(coords), requires_grad=True, dtype=torch.float)
values = torch.tensor(
values, requires_grad=False, dtype=torch.float)
# Making some preditions,
predicted_values = model(coords).squeeze()
# and find their loss.
loss = F.mse_loss(predicted_values, values)
# Walk down the hill of righteousness!
optimizer.zero_grad()
loss.backward()
optimizer.step()
if debug:
print(">>> Coords: {}".format(coords))
print(">>> Values: {}".format(values))
print(">>> Predicted values: {}".format(values))
print(">>> Loss {}".format(loss))
# Use the trained strategist to generate a bias_board,
bias_board = create_bias_board(m, n, model)
if tensorboard and (int(episode) % update_every) == 0:
writer.add_scalar(
os.path.join(tensorboard, 'error'), loss, episode)
plot_wythoff_board(
strategic_value,
vmin=0,
vmax=1,
path=tensorboard,
name='strategy_board_{}.png'.format(episode))
writer.add_image(
'Training board',
skimage.io.imread(
os.path.join(tensorboard,
'strategy_board_{}.png'.format(episode))))
plot_wythoff_board(
bias_board,
vmin=0,
vmax=1,
path=tensorboard,
name='bias_board_{}.png'.format(episode))
writer.add_image(
'Testing board',
skimage.io.imread(
os.path.join(tensorboard,
'bias_board_{}.png'.format(episode))))
# The end
if tensorboard:
writer.close()
# Suppress return for parallel runs?
result = (model), (loss)
if return_none:
result = None
return result
def wythoff_strategist(stumbler_model,
stumbler_game,
num_episodes=1000,
cold_threshold=0.0,
hot_threshold=0.5,
hot_value=1,
cold_value=-1,
learning_rate=0.01,
game='Wythoff50x50',
model=None,
num_hidden1=100,
num_hidden2=25,
initial=0,
score=0.0,
tensorboard=None,
stumbler_mode='numpy',
balance_cold=False,
reflect_cold=True,
update_every=50,
save=None,
load_model=None,
save_model=False,
monitor=None,
return_none=False,
debug=False,
heuristic=True,
seed=None):
"""Learn a generalizable strategy for Wythoffs game"""
# ------------------------------------------------------------------------
# Setup
if tensorboard is not None:
try:
os.makedirs(tensorboard)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
writer = SummaryWriter(log_dir=tensorboard)
# Create env and find all moves in it
# Create env
if tensorboard is not None:
env = create_env(game, monitor=True)
else:
env = create_env(game, monitor=False)
env.seed(seed)
np.random.seed(seed)
o, p, _, _ = peek(create_env(stumbler_game, monitor=False))
m, n, board, _ = peek(env)
all_possible_moves = create_all_possible_moves(m, n)
# Watch vars?
if monitor:
monitored = create_monitored(monitor)
# Init strategist
if model is None:
model = init_strategist(num_hidden1, num_hidden2)
# Add old weights from file?
if load_model is not None:
if debug:
print(">>> Loading model from {}".format(load_model))
model = load_strategist(model, load_model)
# Init SGD.
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# ------------------------------------------------------------------------
# Extract strategic data from the stumbler
strategic_default_value = 0.0
if heuristic:
if hot_threshold is None:
strategic_value = estimate_cold(
m,
n,
stumbler_model,
threshold=cold_threshold,
value=cold_value,
reflect=reflect_cold,
default_value=strategic_default_value)
elif cold_threshold is None:
strategic_value = estimate_hot(
m,
n,
stumbler_model,
threshold=hot_threshold,
value=hot_value,
default_value=strategic_default_value)
else:
strategic_value = estimate_hot_cold(
o,
p,
stumbler_model,
hot_threshold=hot_threshold,
cold_threshold=cold_threshold,
hot_value=hot_value,
cold_value=cold_value,
reflect_cold=reflect_cold,
default_value=strategic_default_value)
else:
strategic_value = expected_value(
o, p, stumbler_model, default_value=strategic_default_value)
# Convert format.
s_data = convert_ijv(strategic_value)
if balance_cold:
s_data = balance_ijv(s_data, cold_value)
# Sanity?
if s_data is None:
return model, None
# Define a memory to sample.
memory = ReplayMemory(len(s_data))
batch_size = len(s_data)
for d in s_data:
memory.push(*d)
# ------------------------------------------------------------------------
# Sample the memory to teach the strategist
bias_board = None
for episode in range(initial, initial + num_episodes):
loss = 0.0
if debug:
print("---------------------------------------")
print(">>> STRATEGIST ({}).".format(episode))
coords = []
values = []
for c, v in memory.sample(batch_size):
coords.append(c)
values.append(v)
coords = torch.tensor(
np.vstack(coords), requires_grad=True, dtype=torch.float)
values = torch.tensor(values, requires_grad=False, dtype=torch.float)
# Making some preditions, ...
predicted_values = model(coords).squeeze()
# and learn from them
loss = F.mse_loss(predicted_values, values)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# --------------------------------------------------------------------
if debug:
print(">>> Coords: {}".format(coords))
print(">>> Values: {}".format(values))
print(">>> Predicted values: {}".format(values))
print(">>> Loss {}".format(loss))
if tensorboard and (int(episode) % update_every) == 0:
# Timecourse
writer.add_scalar('stategist_error', loss, episode)
bias_board = create_bias_board(m, n, model)
plot_wythoff_board(
bias_board,
vmin=-1.5,
vmax=1.5,
path=tensorboard,
height=10,
width=15,
name='bias_board.png')
writer.add_image(
'strategist',
skimage.io.imread(os.path.join(tensorboard, 'bias_board.png')))
if monitor and (int(episode) % update_every) == 0:
# Score the model:
with th.no_grad():
pred = create_bias_board(m, n, model, default=0.0).numpy()
cold = create_cold_board(m, n, default=hot_value)
mae = np.median(np.abs(pred - cold))
all_variables = locals()
for k in monitor:
monitored[k].append(float(all_variables[k]))
# Final score for the model:
with th.no_grad():
pred = create_bias_board(m, n, model, default=0.0).numpy()
cold = create_cold_board(m, n, default=hot_value)
mae = np.median(np.abs(pred - cold))
# Save?
if save_model:
state = {
'strategist_model_dict': model.state_dict(),
"num_hidden1": num_hidden1,
"num_hidden2": num_hidden2
}
th.save(state, save + ".pytorch")
if monitor:
save_monitored(save, monitored)
# Suppress return for parallel runs?
result = (model), (mae)
if return_none:
result = None
return result
def wythoff_stumbler(num_episodes=10,
epsilon=0.1,
gamma=0.8,
learning_rate=0.1,
game='Wythoff10x10',
model=None,
opponent=None,
anneal=False,
bias_board=None,
influence=0.0,
score=0.0,
total_reward=0.0,
tensorboard=None,
update_every=5,
initial=0,
self_play=False,
save=False,
load_model=None,
save_model=False,
monitor=None,
return_none=False,
debug=False,
seed=None):
"""Learn to play Wythoff's w/ e-greedy random exploration.
Note: Learning is based on a player-opponent joint action formalism
and tabular Q-learning.
"""
# ------------------------------------------------------------------------
# Init env
if tensorboard is not None:
try:
os.makedirs(tensorboard)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
writer = SummaryWriter(log_dir=tensorboard)
# Create env
if tensorboard is not None:
env = create_env(game, monitor=True)
else:
env = create_env(game, monitor=False)
env.seed(seed)
np.random.seed(seed)
if monitor is not None:
monitored = create_monitored(monitor)
# ------------------------------------------------------------------------
# Init Agents
default_Q = 0.0
m, n, board, available = peek(env)
if model is None:
model = {}
if opponent is None:
opponent = {}
# Override from file?
if load_model is not None:
if debug:
print(">>> Loadiing model/opponent from {}".format(load_model))
model, opponent = load_stumbler(model, opponent, load_model)
# ------------------------------------------------------------------------
for episode in range(initial, initial + num_episodes):
# Re-init
steps = 1
x, y, board, available = env.reset()
board = tuple(flatten_board(board))
if debug:
print("---------------------------------------")
print(">>> NEW GAME ({}).".format(episode))
print(">>> Initial position ({}, {})".format(x, y))
print(">>> Initial moves {}".format(available))
print("---------------------------------------")
t_state = [
board,
]
t_available = [available]
t_move = []
t_move_i = []
t_reward = []
# -------------------------------------------------------------------
# Anneal epsilon?
if anneal:
epsilon_e = epsilon * (1.0 / np.log((episode + np.e)))
else:
epsilon_e = episode
# -------------------------------------------------------------------
# Play!
done = False
player_win = False
while not done:
# PLAYER CHOOSES A MOVE
try:
Qs_episode = add_bias_board(model[board], available,
bias_board, influence)
move_i = epsilon_greedy(
Qs_episode, epsilon=epsilon_e, mode='numpy')
except KeyError:
model[board] = np.ones(len(available)) * default_Q
move_i = np.random.randint(0, len(available))
move = available[move_i]
# Analyze it...
best = 0.0
if cold_move_available(x, y, available):
if move in locate_cold_moves(x, y, available):
best = 1.0
score += (best - score) / (episode + 1)
# PLAY THE MOVE
(x, y, board, available), reward, done, _ = env.step(move)
board = tuple(flatten_board(board))
steps += 1
# Log....
if debug:
print(">>> PLAYER move {}".format(move))
t_state.append(board)
t_move.append(move)
t_available.append(available)
t_move_i.append(move_i)
t_reward.append(reward)
if done:
player_win = True
t_state.append(board)
t_move.append(move)
t_available.append(available)
t_move_i.append(move_i)
t_reward.append(reward)
# ----------------------------------------------------------------
if not done:
# OPPONENT CHOOSES A MOVE
try:
Qs_episode = add_bias_board(opponent[board], available,
bias_board, influence)
move_i = epsilon_greedy(
Qs_episode, epsilon=epsilon_e, mode='numpy')
except KeyError:
opponent[board] = np.ones(len(available)) * default_Q
move_i = np.random.randint(0, len(available))
move = available[move_i]
# PLAY THE MOVE
(x, y, board, available), reward, done, _ = env.step(move)
board = tuple(flatten_board(board))
steps += 1
# Log....
if debug:
print(">>> OPPONENT move {}".format(move))
t_state.append(board)
t_move.append(move)
t_available.append(available)
t_move_i.append(move_i)
t_reward.append(reward)
if done:
t_state.append(board)
t_move.append(move)
t_available.append(available)
t_move_i.append(move_i)
t_reward.append(reward)
# ----------------------------------------------------------------
# Learn by unrolling the last game...
# PLAYER (model)
s_idx = np.arange(0, steps - 1, 2)
for i in s_idx:
# States and actions
s = t_state[i]
next_s = t_state[i + 2]
m_i = t_move_i[i]
# Value and reward
Q = model[s][m_i]
try:
max_Q = model[next_s].max()
except KeyError:
model[next_s] = np.ones(len(t_available[i])) * default_Q
max_Q = model[next_s].max()
if player_win:
r = t_reward[i]
else:
r = -1 * t_reward[i + 1]
# Update running reward total for player
total_reward += r
# Loss and learn
next_Q = r + (gamma * max_Q)
loss = next_Q - Q
model[s][m_i] = Q + (learning_rate * loss)
# OPPONENT
s_idx = np.arange(1, steps - 1, 2)
for i in s_idx:
# States and actions
s = t_state[i]
next_s = t_state[i + 2]
m_i = t_move_i[i]
# Value and reward
Q = opponent[s][m_i]
try:
max_Q = opponent[next_s].max()
except KeyError:
opponent[next_s] = np.ones(len(t_available[i])) * default_Q
max_Q = opponent[next_s].max()
if not player_win:
r = t_reward[i]
else:
r = -1 * t_reward[i + 1]
# Loss and learn
next_Q = r + (gamma * max_Q)
loss = next_Q - Q
opponent[s][m_i] = Q + (learning_rate * loss)
# ----------------------------------------------------------------
# Update the log
if debug:
print(">>> Reward {}; Loss(Q {}, next_Q {}) -> {}".format(
r, Q, next_Q, loss))
if done and (r > 0):
print("*** WIN ***")
if done and (r < 0):
print("*** OPPONENT WIN ***")
if tensorboard and (int(episode) % update_every) == 0:
writer.add_scalar('reward', r, episode)
writer.add_scalar('Q', Q, episode)
writer.add_scalar('epsilon_e', epsilon_e, episode)
writer.add_scalar('stumber_error', loss, episode)
writer.add_scalar('stumber_steps', steps, episode)
writer.add_scalar('stumbler_score', score, episode)
# Cold ref:
cold = create_cold_board(m, n)
plot_wythoff_board(
cold, vmin=0, vmax=1, path=tensorboard, name='cold_board.png')
writer.add_image(
'cold_positions',
skimage.io.imread(os.path.join(tensorboard, 'cold_board.png')))
# Agent max(Q) boards
values = expected_value(m, n, model)
plot_wythoff_board(
values, path=tensorboard, name='player_max_values.png')
writer.add_image(
'player',
skimage.io.imread(
os.path.join(tensorboard, 'player_max_values.png')))
values = expected_value(m, n, opponent)
plot_wythoff_board(
values, path=tensorboard, name='opponent_max_values.png')
writer.add_image(
'opponent',
skimage.io.imread(
os.path.join(tensorboard, 'opponent_max_values.png')))
if monitor and (int(episode) % update_every) == 0:
all_variables = locals()
for k in monitor:
monitored[k].append(float(all_variables[k]))
# --------------------------------------------------------------------
if save_model:
state = {
'stumbler_player_dict': model,
'stumbler_opponent_dict': opponent
}
torch.save(state, save + ".pytorch")
if monitor:
save_monitored(save, monitored)
if tensorboard:
writer.close()
result = (model, opponent), (score, total_reward)
if return_none:
result = None
return result
