class PongTestbench(object):
def __init__(self, render=False):
self.player1 = None
self.player2 = None
self.total_games = 0
self.wins1 = 0
self.wins2 = 0
self.render = render
self.env = gym.make("WimblepongVisualMultiplayer-v0")
def init_players(self, player1, player2=None):
self.player1 = player1
if player2:
self.player2 = player2
else:
self.player2 = SimpleAi(self.env, player_id=2)
self.set_names()
def switch_sides(self):
def switch_simple_ai(player):
if type(player) is SimpleAi:
player.player_id = 3 - player.player_id
op1 = self.player1
ow1 = self.wins1
self.player1 = self.player2
self.wins1 = self.wins2
self.player2 = op1
self.wins2 = ow1
# Ensure SimpleAi knows where it's playing
switch_simple_ai(self.player1)
switch_simple_ai(self.player2)
self.env.switch_sides()
print("Switching sides.")
def play_game(self):
self.player1.reset()
self.player2.reset()
obs1, obs2 = self.env.reset()
done = False
while not done:
action1 = self.player1.get_action(obs1)
action2 = self.player2.get_action(obs2)
(obs1, obs2), (rew1, rew2), done, info = self.env.step(
(action1, action2))
if self.render:
self.env.render()
if done:
if rew1 > 0:
self.wins1 += 1
elif rew2 > 0:
self.wins2 += 1
else:
raise ValueError("Game finished but no one won?")
self.total_games += 1
# print("Game %d finished." % self.total_games)
def run_test(self, no_games=100, switch_freq=-1):
# Ensure the testbench is in clear state
assert self.wins1 is 0 and self.wins2 is 0 and self.total_games is 0
if switch_freq == -1:
# Switch once in the middle
switch_freq = no_games // 2
elif switch_freq in (None, 0):
# Don't switch sides at all
switch_freq = no_games * 2
print("Running test: %s vs %s." %
(self.player1.get_name(), self.player2.get_name()))
for i in range(no_games):
self.play_game()
if i % switch_freq == switch_freq - 1:
self.switch_sides()
# Ensure correct state
assert self.wins1 + self.wins2 == self.total_games
print("Test results:")
print("%s vs %s" % (self.player1.get_name(), self.player2.get_name()))
print("%d : %d" % (self.wins1, self.wins2))
print("-" * 40)
return self.wins1
def set_names(self):
def verify_name(name):
# TODO: some ASCII/profanity checks?
return type(name) is str and 0 < len(name) <= 26
name1 = self.player1.get_name()
name2 = self.player2.get_name()
if not verify_name(name1):
raise ValueError("Name", name1, "not correct")
if not verify_name(name2):
raise ValueError("Name", name2, "not correct")
self.env.set_names(name1, name2)
def get_agent_score(self, agent):
if agent is self.player1:
return self.wins1, self.total_games
elif agent is self.player2:
return self.wins2, self.total_games
else:
raise ValueError("Agent not found in the testbench!")
class PongTrainbench(object):
def __init__(self, render=False):
self.player1 = None
self.player2 = None
self.total_games = 0
self.wins1 = 0
self.wins2 = 0
self.render = render
self.env = gym.make("WimblepongVisualMultiplayer-v0")
self.me = 0
self.sample_count = 0
def init_players(self, player1, player2=None):
if player1:
self.player1 = player1
else:
self.player1 = SimpleAi(self.env, player_id=1)
if player2:
self.player2 = player2
else:
self.player2 = SimpleAi(self.env, player_id=2)
self.set_names()
def switch_sides(self):
def switch_simple_ai(player):
if type(player) is SimpleAi:
player.player_id = 3 - player.player_id
op1 = self.player1
ow1 = self.wins1
self.player1 = self.player2
self.wins1 = self.wins2
self.player2 = op1
self.wins2 = ow1
self.me += 1
# Ensure SimpleAi knows where it's playing
switch_simple_ai(self.player1)
switch_simple_ai(self.player2)
self.env.switch_sides()
print("Switching sides.")
def prepro(self, I):
""" prepro 200x200x3 uint8 frame into 10000 (10x10) 1D float vector """
I = I[::2, ::2, 0] # downsample by factor of 2
I[I == 43] = 0 # erase background (background type 1)
I[I != 0] = 1 # everything else (paddles, ball) just set to 1
return I.astype(np.float).ravel()
def play_game(self, samples):
self.player1.reset()
self.player2.reset()
obs1, obs2 = self.env.reset()
done = False
while not done:
action1 = self.player1.get_action(obs1)
action2 = self.player2.get_action(obs2)
if (self.me % 2 == 0):
player = self.env.player1
opponent = self.env.player2
else:
player = self.env.player2
opponent = self.env.player1
my_y = player.y
op_y = opponent.y
ball_x = self.env.ball.x
ball_y = self.env.ball.y
#print(my_y, op_y, ball_x, ball_y)
#plt.imshow(obs1)
#plt.show()
my_obs = self.prepro(obs1)
my_obs = np.array(my_obs)
#print(my_obs.shape)
sample = [my_obs, my_y, op_y, ball_x, ball_y]
#print(sample)
samples.append(sample)
self.sample_count += 1
(obs1, obs2), (rew1, rew2), done, info = self.env.step(
(action1, action2))
if self.render:
self.env.render()
if done:
if rew1 > 0:
self.wins1 += 1
elif rew2 > 0:
self.wins2 += 1
else:
raise ValueError("Game finished but no one won?")
self.total_games += 1
# print("Game %d finished." % self.total_games)
return samples
def run_test(self, no_games=100, switch_freq=-1):
# Ensure the testbench is in clear state
assert self.wins1 is 0 and self.wins2 is 0 and self.total_games is 0
if switch_freq == -1:
# Switch once in the middle
switch_freq = no_games // 2
elif switch_freq in (None, 0):
# Don't switch sides at all
switch_freq = no_games * 2
print("Running test: %s vs %s." %
(self.player1.get_name(), self.player2.get_name()))
outputfile = "test_samples.p"
try:
with open(('./' + outputfile), "rb") as input_file:
samples = pickle.load(input_file)
except (OSError, IOError) as e:
with open(('./' + outputfile), "wb") as output_file:
samples = []
pickle.dump(samples, output_file)
while self.sample_count < (no_games):
samples = self.play_game(samples)
print("samples", self.sample_count)
with open(('./' + outputfile), "wb") as output_file:
pickle.dump(samples, output_file)
with open(('./' + outputfile), "rb") as input_file:
samples = pickle.load(input_file)
samples = np.array(samples)
print(samples.shape)
print(samples)
def set_names(self):
def verify_name(name):
# TODO: some ASCII/profanity checks?
return type(name) is str and 0 < len(name) <= 26
name1 = self.player1.get_name()
name2 = self.player2.get_name()
if not verify_name(name1):
raise ValueError("Name", name1, "not correct")
if not verify_name(name2):
raise ValueError("Name", name2, "not correct")
self.env.set_names(name1, name2)
def get_agent_score(self, agent):
if agent is self.player1:
return self.wins1, self.total_games
elif agent is self.player2:
return self.wins2, self.total_games
else:
raise ValueError("Agent not found in the testbench!")
class PongTrainbench(object):
def __init__(self, render=False):
self.player1 = None
self.player2 = None
self.total_games = 0
self.wins1 = 0
self.wins2 = 0
self.render = render
self.env = gym.make("WimblepongVisualMultiplayer-v0")
self.me = 0
self.sample_count = 0
def init_players(self, player1, player2=None):
if player1:
self.player1 = player1
else:
self.player1 = SimpleAi(self.env, player_id=1)
if player2:
self.player2 = player2
else:
self.player2 = SimpleAi(self.env, player_id=2)
self.set_names()
def switch_sides(self):
def switch_simple_ai(player):
if type(player) is SimpleAi:
player.player_id = 3 - player.player_id
op1 = self.player1
ow1 = self.wins1
self.player1 = self.player2
self.wins1 = self.wins2
self.player2 = op1
self.wins2 = ow1
self.me += 1
# Ensure SimpleAi knows where it's playing
switch_simple_ai(self.player1)
switch_simple_ai(self.player2)
self.env.switch_sides()
print("Switching sides.")
def prepro(self, I):
""" prepro 200x200x3 uint8 frame into 10000 (10x10) 1D float vector """
I = I[::2, ::2, 0] # downsample by factor of 2
I[I == 43] = 0 # erase background (background type 1)
I[I != 0] = 1 # everything else (paddles, ball) just set to 1
return I.astype(np.float).ravel()
def play_game(self):
self.player1.reset()
self.player2.reset()
obs1, obs2 = self.env.reset()
done = False
samples = []
while not done:
action1 = self.player1.get_action(obs1)
action2 = self.player2.get_action(obs2)
if (self.me % 2 == 0):
player = self.env.player1
opponent = self.env.player2
else:
player = self.env.player2
opponent = self.env.player1
my_y = player.y
op_y = opponent.y
ball_x = self.env.ball.x
ball_y = self.env.ball.y
#print(my_y, op_y, ball_x, ball_y)
#plt.imshow(obs1)
#plt.show()
my_obs = self.prepro(obs1)
my_obs = np.array(my_obs)
#print(my_obs.shape)
sample = [my_obs, my_y, op_y, ball_x, ball_y]
#print(sample)
samples.append(sample)
self.sample_count += 1
(obs1, obs2), (rew1, rew2), done, info = self.env.step(
(action1, action2))
if self.render:
self.env.render()
if done:
if rew1 > 0:
self.wins1 += 1
elif rew2 > 0:
self.wins2 += 1
else:
raise ValueError("Game finished but no one won?")
self.total_games += 1
# print("Game %d finished." % self.total_games)
return samples
def run_test(self, no_games=100, switch_freq=-1):
# Ensure the testbench is in clear state
assert self.wins1 is 0 and self.wins2 is 0 and self.total_games is 0
if switch_freq == -1:
# Switch once in the middle
switch_freq = no_games // 2
elif switch_freq in (None, 0):
# Don't switch sides at all
switch_freq = no_games * 2
print("Running test: %s vs %s." %
(self.player1.get_name(), self.player2.get_name()))
input_dim = 10000
output_dim = 4
#print(input_dim, output_dim)
# another way to define a network
net = torch.nn.Sequential(
torch.nn.Linear(input_dim, 200),
torch.nn.LeakyReLU(),
torch.nn.Linear(200, 100),
torch.nn.LeakyReLU(),
torch.nn.Linear(100, output_dim),
)
model_file = "./supervised_model.pth"
loss_file = "./losses.p"
try:
net.load_state_dict(torch.load(model_file))
#model.eval()
except (OSError, IOError) as e:
pass
try:
with open((loss_file), "rb") as input_file:
plot_losses = pickle.load(input_file)
except (OSError, IOError) as e:
plot_losses = []
with open((loss_file), "wb") as output_file:
pickle.dump(plot_losses, output_file)
optimizer = torch.optim.Adam(net.parameters(), lr=0.01)
loss_func = torch.nn.MSELoss(
) # this is for regression mean squared loss
games = 0
running_loss = 0
print_freq = 1
while games <= (no_games):
samples = self.play_game()
#print("samples ",self.sample_count)
samples = np.array(samples)
x = samples[:, 0]
x = x.reshape(x.shape[0], -1)
#print(x.shape)
y = samples[:, 1:]
y = y.reshape(x.shape[0], -1)
#print(y.shape)
new_x = np.zeros((x.shape[0], (x[0][0]).shape[0]))
new_y = np.zeros((y.shape[0], (y).shape[1]))
#print(y)
for i in range(x.shape[0]):
new_x[i, :] = x[i][0]
new_y[i, :] = y[i]
x = torch.Tensor(new_x)
y = torch.Tensor(new_y)
prediction = net(x)
loss = loss_func(prediction, y)
running_loss += loss
optimizer.zero_grad() # clear gradients for next train
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
if (games % print_freq == 0 and games != 0):
print("game", games, " | avg loss in last 100 games: ",
(running_loss / print_freq))
plot_losses.append(running_loss)
running_loss = 0
torch.save(net.state_dict(), model_file)
plt.plot(plot_losses, 'b')
plt.savefig('./losses.png')
with open((loss_file), "wb") as output_file:
pickle.dump(plot_losses, output_file)
games += 1
torch.save(net.state_dict(), model_file)
with open((loss_file), "wb") as output_file:
pickle.dump(plot_losses, output_file)
plt.plot(plot_losses, 'b')
plt.savefig('./losses.png')
plt.show()
plt.plot(plot_losses[10:], 'b')
plt.show()
def set_names(self):
def verify_name(name):
# TODO: some ASCII/profanity checks?
return type(name) is str and 0 < len(name) <= 26
name1 = self.player1.get_name()
name2 = self.player2.get_name()
if not verify_name(name1):
raise ValueError("Name", name1, "not correct")
if not verify_name(name2):
raise ValueError("Name", name2, "not correct")
self.env.set_names(name1, name2)
def get_agent_score(self, agent):
if agent is self.player1:
return self.wins1, self.total_games
elif agent is self.player2:
return self.wins2, self.total_games
else:
raise ValueError("Agent not found in the testbench!")
import matplotlib.pyplot as plt
#%%
env = gym.make("WimblepongVisualMultiplayer-v0")
#%%
# Parameters
render = False
num_iterations = 100000
# Define the player IDs for both SimpleAI agents
player_id = 1
opponent_id = 3 - player_id
opponent = SimpleAi(env, opponent_id)
player = Agent(env, player_id)
# Set the names for both SimpleAIs
env.set_names(player.get_name(), opponent.get_name())
(ob1, ob2), (rew1, rew2), done, info = env.step((2, 2))
win1 = 0
culmulative_reward = 0
length_history = []
win_history = []
mean_winrate_history = []
mean_length_history = []
for it in range(num_iterations):
for ep in range(200):
done = False
length_ep = 0
# Reset the environment and observe the initial state