def eval(model_type=model_type, model_path=model_path):
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
env = LunarLander()
if model_type == 'policy':
model = Policy(env.observation_dim, env.action_dim)
elif model_type == 'dqn':
model = Network(env.observation_dim, env.action_dim)
model.to(device)
model.load_state_dict(torch.load(model_path))
model.eval()
episodes = 50
wins = 0
frames = []
fuel_left = []
for i in range(episodes):
if i % 10 == 0:
print(f"On episode {i}")
frame_count = 0
env.reset()
state = env.get_state()
while True:
frame_count += 1
action = model(
torch.tensor(state, dtype=torch.float32,
device=device).unsqueeze(0)).argmax()
state, reward, done = env.step(action)
if done:
if env.won:
wins += 1
frames.append(frame_count)
fuel_left.append(env.rocket.fuel)
break
env.close()
if wins > 0:
print(f"wins: {wins}")
print(f"mean frames on wins {np.mean(frames)}")
print(f"std frames on wins {np.std(frames, ddof=1)}")
print(f"min frames on wins {np.min(frames)}")
print(f"max frames on wins {np.max(frames)}")
print(f"mean fuel on wins {np.mean(fuel_left)}")
print(f"std fuel on wins {np.std(fuel_left, ddof=1)}")
print(f"min fuel on wins {np.min(fuel_left)}")
print(f"max fuel on wins {np.max(fuel_left)}")
else:
print("The model had 0 wins. Statistics can't be calculated")
def __init__(
self,
env,
memory_size,
batch_size,
target_update=100,
gamma=0.99,
# replay parameters
alpha=0.2,
beta=0.6,
prior_eps=1e-6,
# Categorical DQN parameters
v_min=0,
v_max=200,
atom_size=51,
# N-step Learning
n_step=3,
start_train=32,
save_weights=True,
log=True,
lr=0.001,
seed=0,
episodes=200):
self.env = env
obs_dim = self.env.observation_dim
action_dim = self.env.action_dim
self.batch_size = batch_size
self.target_update = target_update
self.gamma = gamma
self.lr = lr
self.memory_size = memory_size
self.seed = seed
# device: cpu / gpu
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
print(self.device)
# memory for 1-step Learning
self.beta = beta
self.prior_eps = prior_eps
self.memory = PrioritizedReplayBuffer(obs_dim,
memory_size,
batch_size,
alpha=alpha)
# memory for N-step Learning
self.use_n_step = True if n_step > 1 else False
if self.use_n_step:
self.n_step = n_step
self.memory_n = ReplayBuffer(obs_dim,
memory_size,
batch_size,
n_step=n_step,
gamma=gamma)
# Categorical DQN parameters
self.v_min = v_min
self.v_max = v_max
self.atom_size = atom_size
self.support = torch.linspace(self.v_min, self.v_max,
self.atom_size).to(self.device)
# networks: dqn, dqn_target
self.dqn = Network(obs_dim, action_dim, self.atom_size,
self.support).to(self.device)
self.dqn_target = Network(obs_dim, action_dim, self.atom_size,
self.support).to(self.device)
self.dqn_target.load_state_dict(self.dqn.state_dict())
self.dqn_target.eval()
# optimizer
self.optimizer = optim.Adam(self.dqn.parameters(), lr=self.lr)
# transition to store in memory
self.transition = list()
self.fig, (self.ax1, self.ax2) = plt.subplots(2, figsize=(10, 10))
self.start_train = start_train
self.save_weights = save_weights
self.time = datetime.datetime.now().timetuple()
self.path = f"weights/{self.time[2]}-{self.time[1]}-{self.time[0]}_{self.time[3]}-{self.time[4]}"
self.log = log
self.episode_cnt = 0
self.episodes = episodes
if self.save_weights is True:
self.create_save_directory()
plt.ion()
class DQNAgent:
def __init__(
self,
env,
memory_size,
batch_size,
target_update=100,
gamma=0.99,
# replay parameters
alpha=0.2,
beta=0.6,
prior_eps=1e-6,
# Categorical DQN parameters
v_min=0,
v_max=200,
atom_size=51,
# N-step Learning
n_step=3,
start_train=32,
save_weights=True,
log=True,
lr=0.001,
seed=0,
episodes=200):
self.env = env
obs_dim = self.env.observation_dim
action_dim = self.env.action_dim
self.batch_size = batch_size
self.target_update = target_update
self.gamma = gamma
self.lr = lr
self.memory_size = memory_size
self.seed = seed
# device: cpu / gpu
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
print(self.device)
# memory for 1-step Learning
self.beta = beta
self.prior_eps = prior_eps
self.memory = PrioritizedReplayBuffer(obs_dim,
memory_size,
batch_size,
alpha=alpha)
# memory for N-step Learning
self.use_n_step = True if n_step > 1 else False
if self.use_n_step:
self.n_step = n_step
self.memory_n = ReplayBuffer(obs_dim,
memory_size,
batch_size,
n_step=n_step,
gamma=gamma)
# Categorical DQN parameters
self.v_min = v_min
self.v_max = v_max
self.atom_size = atom_size
self.support = torch.linspace(self.v_min, self.v_max,
self.atom_size).to(self.device)
# networks: dqn, dqn_target
self.dqn = Network(obs_dim, action_dim, self.atom_size,
self.support).to(self.device)
self.dqn_target = Network(obs_dim, action_dim, self.atom_size,
self.support).to(self.device)
self.dqn_target.load_state_dict(self.dqn.state_dict())
self.dqn_target.eval()
# optimizer
self.optimizer = optim.Adam(self.dqn.parameters(), lr=self.lr)
# transition to store in memory
self.transition = list()
self.fig, (self.ax1, self.ax2) = plt.subplots(2, figsize=(10, 10))
self.start_train = start_train
self.save_weights = save_weights
self.time = datetime.datetime.now().timetuple()
self.path = f"weights/{self.time[2]}-{self.time[1]}-{self.time[0]}_{self.time[3]}-{self.time[4]}"
self.log = log
self.episode_cnt = 0
self.episodes = episodes
if self.save_weights is True:
self.create_save_directory()
plt.ion()
def create_save_directory(self):
try:
os.mkdir(self.path)
except OSError:
print("Creation of the directory %s failed" % self.path)
else:
print("Successfully created the directory %s " % self.path)
def select_action(self, state):
"""Select an action from the input state."""
# NoisyNet: no epsilon greedy action selection
selected_action = self.dqn(torch.FloatTensor(state).to(
self.device)).argmax()
selected_action = selected_action.detach().cpu().numpy()
self.transition = [state, selected_action]
return selected_action
def step(self, action):
"""Take an action and return the response of the env."""
next_state, reward, done = self.env.step(action)
self.transition += [reward, next_state, done]
# N-step transition
if self.use_n_step:
one_step_transition = self.memory_n.store(*self.transition)
# 1-step transition
else:
one_step_transition = self.transition
# add a single step transition
if one_step_transition:
self.memory.store(*one_step_transition)
return next_state, reward, done
def update_model(self):
"""Update the model by gradient descent."""
# PER needs beta to calculate weights
samples = self.memory.sample_batch(self.beta)
weights = torch.FloatTensor(samples["weights"].reshape(-1, 1)).to(
self.device)
indices = samples["indices"]
# 1-step Learning loss
elementwise_loss = self._compute_dqn_loss(samples, self.gamma)
# PER: importance sampling before average
loss = torch.mean(elementwise_loss * weights)
# N-step Learning loss
# we are gonna combine 1-step loss and n-step loss so as to
# prevent high-variance. The original rainbow employs n-step loss only.
if self.use_n_step:
gamma = self.gamma**self.n_step
samples = self.memory_n.sample_batch_from_idxs(indices)
elementwise_loss_n_loss = self._compute_dqn_loss(samples, gamma)
elementwise_loss += elementwise_loss_n_loss
# PER: importance sampling before average
loss = torch.mean(elementwise_loss * weights)
self.optimizer.zero_grad()
loss.backward()
# print(loss)
clip_grad_norm_(self.dqn.parameters(), 10.0)
self.optimizer.step()
# PER: update priorities
loss_for_prior = elementwise_loss.detach().cpu().numpy()
new_priorities = loss_for_prior + self.prior_eps
self.memory.update_priorities(indices, new_priorities)
# NoisyNet: reset noise
self.dqn.reset_noise()
self.dqn_target.reset_noise()
return loss.item()
def train(self, num_frames, plotting_interval=100):
"""Train the agent."""
if self.log:
pass
# config = {'gamma': self.gamma, 'log_interval': plotting_interval, 'learning_rate': self.lr,
# 'directory': self.path, 'type': 'dqn', 'replay_memory': self.memory_size, 'environment': 'normal', 'seed': self.seed}
# wandb.init(project='is_os', entity='pydqn', config=config, notes=self.env.reward_function, reinit=True, tags=['report'])
# wandb.watch(self.dqn)
self.env.reset()
state = self.env.get_state()
won = False
update_cnt = 0
losses = []
scores = []
score = 0
frame_cnt = 0
self.episode_cnt = 0
for frame_idx in range(1, num_frames + 1):
frame_cnt += 1
action = self.select_action(state)
next_state, reward, done = self.step(action)
state = next_state
score += reward
fraction = min(frame_cnt / num_frames, 1.0)
self.beta = self.beta + fraction * (1.0 - self.beta)
# if agent has trained 500 frames, terminate
if frame_cnt == 500:
done = True
# if episode ends
if done:
if reward > 0:
won = True
self.env.reset()
state = self.env.get_state()
self.episode_cnt += 1
scores.append(score)
score = 0
frame_cnt = 0
# if training is ready
if len(self.memory) >= self.batch_size:
loss = self.update_model()
losses.append(loss)
update_cnt += 1
# if hard update is needed
if update_cnt % self.target_update == 0:
self._target_hard_update()
# plotting
if frame_idx % plotting_interval == 0:
self._plot(frame_idx, scores, losses)
if frame_idx % 1000 == 0:
torch.save(self.dqn.state_dict(),
f'{self.path}/{frame_idx}.tar')
print(f"model saved at:\n {self.path}/{frame_idx}.tar")
# wandb.run.summary['won'] = won
self.env.close()
def _compute_dqn_loss(self, samples, gamma):
"""Return categorical dqn loss."""
device = self.device # for shortening the following lines
state = torch.FloatTensor(samples["obs"]).to(device)
next_state = torch.FloatTensor(samples["next_obs"]).to(device)
action = torch.LongTensor(samples["acts"]).to(device)
reward = torch.FloatTensor(samples["rews"].reshape(-1, 1)).to(device)
done = torch.FloatTensor(samples["done"].reshape(-1, 1)).to(device)
# Categorical DQN algorithm
delta_z = float(self.v_max - self.v_min) / (self.atom_size - 1)
with torch.no_grad():
# Double DQN
next_action = self.dqn(next_state).argmax(1)
next_dist = self.dqn_target.dist(next_state)
next_dist = next_dist[range(self.batch_size), next_action]
t_z = reward + (1 - done) * gamma * self.support
t_z = t_z.clamp(min=self.v_min, max=self.v_max)
b = (t_z - self.v_min) / delta_z
l = b.floor().long()
u = b.ceil().long()
offset = (torch.linspace(
0, (self.batch_size - 1) * self.atom_size,
self.batch_size).long().unsqueeze(1).expand(
self.batch_size, self.atom_size).to(self.device))
proj_dist = torch.zeros(next_dist.size(), device=self.device)
proj_dist.view(-1).index_add_(0, (l + offset).view(-1),
(next_dist *
(u.float() - b)).view(-1))
proj_dist.view(-1).index_add_(0, (u + offset).view(-1),
(next_dist *
(b - l.float())).view(-1))
dist = self.dqn.dist(state)
log_p = torch.log(dist[range(self.batch_size), action])
elementwise_loss = -(proj_dist * log_p).sum(1)
return elementwise_loss
def _target_hard_update(self):
"""Hard update: target <- local."""
self.dqn_target.load_state_dict(self.dqn.state_dict())
def _plot(self, frame_cnt, scores, losses):
self.ax1.cla()
self.ax1.set_title(
f'frames: {frame_cnt} score: {np.mean(scores[-10:])}')
self.ax1.plot(scores[-999:], color='red')
self.ax2.cla()
self.ax2.set_title(f'loss: {np.mean(losses[-10:])}')
self.ax2.plot(losses[-999:], color='blue')
plt.show()
plt.pause(0.1)
# needed for wandb to not log nans
# if frame_cnt < self.start_train + 11:
# loss = 0
# else:
# loss = np.mean(losses[-10:])
if self.log:
pass
def runMission(train=False, load_model=False):
'''
Run or train Deep Model
Training method still needs to be added
'''
# Global timer - multi purpose
start_time = time.time()
print("\n ---- Running the Deep Q Network ----- \n")
USE_SAVED_MODEL_FILE = False
# agent_host, my_mission, my_mission_record, action_space = setupMinecraft()
model = Network(local_size=LOCAL_GRID_SIZE,
name=MODEL_NAME,
path="./Models/Tensorflow/" + FOLDER + "/",
load=load_model,
trainable=train)
brain = Brain(epsilon=0.1, action_space=5)
model.setup(brain)
tf.summary.scalar('error', tf.squeeze(model.error))
avg_time = 0
avg_score = 0
avg_error = 0
avg_reward = 0
cumulative_reward = 0
print_episode = 1
total_episodes = 10
# Saving model capabilities
saver = tf.train.Saver()
# Initialising all variables (weights and biases)
init = tf.global_variables_initializer()
# Adds a summary graph of the error over time
merged_summary = tf.summary.merge_all()
# Tensorboard capabilties
writer = tf.summary.FileWriter(LOGDIR)
# Assume that you have 12GB of GPU memory and want to allocate ~4GB:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
# Begin Session
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# Restore the model, to keep training
if USE_SAVED_MODEL_FILE:
saver.restore(sess, MODEL_CHECKPOINT)
print("Model restored.")
else:
# Initialize global variables
sess.run(init)
# Tensorboard graph
writer.add_graph(sess.graph)
print("\nProgram took {0:.4f} seconds to initialise\n".format(
time.time() - start_time))
start_time = time.time()
# Running mission
for episode in range(total_episodes):
agent_host, my_mission, my_mission_record, action_space = setupMinecraft(
)
world_state = reset(agent_host, my_mission, my_mission_record)
score = 0
done = False
craft_sword = False
# Getting first observation
while True:
world_state = agent_host.getWorldState()
if world_state.number_of_observations_since_last_state > 0:
break
msg = world_state.observations[-1].text
observations = json.loads(msg)
# grid = observations.get(u'floor9x9', 0)
grid = observations.get(u'floor15x15', 0)
score = observations.get(u'Hotbar_8_size', 0)
nearby_entites = observations.get(u'nearby_entites', 0)
diamonds = []
zombies = []
steve_pos = (0, 0)
steve_life = 20
for entity in nearby_entites:
if entity["name"] == "diamond":
diamonds.append((entity["x"], entity["z"]))
if entity["name"] == "steve":
steve_pos = ((entity["x"], entity["z"]))
steve_life = entity["life"]
if entity["name"] == "Zombie":
zombies.append((entity["x"], entity["z"]))
state = get_state(steve_pos, diamonds, zombies, grid)
# brain.linear_epsilon_decay(total_episodes, episode, start=0.3, end=0.05, percentage=0.5)
world_state = agent_host.getWorldState()
while world_state.is_mission_running and not done:
print("-", end="")
time.sleep(0.01)
action = brain.choose_action(state, sess, model)
# print("action:", action_space[action])
if craft_sword:
agent_host.sendCommand("craft diamond_sword")
done = True
else:
agent_host.sendCommand(action_space[action])
time.sleep(0.2)
world_state = agent_host.getWorldState()
for error in world_state.errors:
print("Error:", error.text)
# Have we received any observations?
if world_state.number_of_observations_since_last_state > 0:
# if world_state.number_of_observations_since_last_state > 0 and world_state.number_of_rewards_since_last_state > 0:
msg = world_state.observations[-1].text
observations = json.loads(msg)
# print("\n\n", observations, "\n\n")
grid = observations.get(u'floor15x15', 0)
score = observations.get(u'Hotbar_8_size', 0)
nearby_entites = observations.get(u'nearby_entites', 0)
diamonds = []
zombies = []
for entity in nearby_entites:
if entity["name"] == "diamond":
diamonds.append((entity["x"], entity["z"]))
if entity["name"] == "Steve":
steve_pos = ((entity["x"], entity["z"]))
steve_life = entity["life"]
if entity["name"] == "Zombie":
zombies.append((entity["x"], entity["z"]))
# Debugging - print the state
for i in range(6):
print(state[i])
print()
new_state = get_state(steve_pos, diamonds, zombies, grid)
# reward = world_state.rewards[-1].getValue()
# score += reward
# brain.store_transition(state, action, reward, done, new_state)
# e, Q_vector = brain.train(model, sess)
state = new_state
# cumulative_reward += reward
# print(score)
if score >= 6:
craft_sword = True
if steve_life != 20:
done = True
# if done:
# avg_time += info["time"]
# avg_score += info["score"]
# avg_error += e
# avg_reward += cumulative_reward
# cumulative_reward = 0
if (episode % print_episode == 0
and episode != 0) or (episode == total_episodes - 1):
current_time = math.floor(time.time() - start_time)
print(
"Ep:",
episode,
"\tavg t: {0:.3f}".format(avg_time / print_episode),
"\tavg score: {0:.3f}".format(avg_score / print_episode),
"\terr {0:.3f}".format(avg_error / print_episode),
"\tavg_reward {0:.3f}".format(
avg_reward / print_episode), # avg cumulative reward
"\tepsilon {0:.3f}".format(brain.EPSILON),
end="")
print_readable_time(current_time)
# Save the model's weights and biases to .npz file
# model.save(sess)
# save_path = saver.save(sess, MODEL_PATH_SAVE)
# s = sess.run(merged_summary, feed_dict={model.input: state, model.actions: Q_vector, score:avg_score/print_episode, avg_t:avg_time/print_episode, epsilon:brain.EPSILON, avg_r:avg_reward/print_episode})
# writer.add_summary(s, episode)
avg_time = 0
avg_score = 0
avg_error = 0
avg_reward = 0
# model.save(sess, verbose=True)
# save_path = saver.save(sess, MODEL_CHECKPOINT)
# print("Model saved in path: %s" % save_path)
writer.close()
def run_MetaNetwork():
print("\n ---- Running the Meta Network ----- \n")
MODEL_NAME = "meta15_input6_4_unfrozen"
DIAMOND_MODEL_NAME = "diamond15_input6_best_unfrozen4_300k"
ZOMBIE_MODEL_NAME = "zombie15_input6_best_unfrozen4_300k"
EXPLORE_MODEL_NAME = "explore15_input6_best_unfrozen4_300k"
MODEL_PATH_SAVE = "./Models/Tensorflow/Meta/"+MODEL_NAME+"/"+MODEL_NAME+".ckpt"
LOGDIR = "./Logs/"+MODEL_NAME
USE_SAVED_MODEL_FILE = False
GRID_SIZE = 10
LOCAL_GRID_SIZE = 15
MAP_PATH = None
RANDOMIZE_MAPS = True
RENDER_TO_SCREEN = False
RENDER_TO_SCREEN = True
env = Environment(wrap = False,
grid_size = GRID_SIZE,
local_size = LOCAL_GRID_SIZE,
rate = 80,
max_time = 100,
food_count = 10,
obstacle_count = 0,
lava_count = 0,
zombie_count = 2,
history = 40,
action_space = 5,
map_path = MAP_PATH)
if RENDER_TO_SCREEN:
env.prerender()
model = MetaNetwork(local_size=LOCAL_GRID_SIZE, name=MODEL_NAME, path="./Models/Tensorflow/Best_Meta/", load=True, trainable = False)
diamond_net = Network(local_size=LOCAL_GRID_SIZE, name=DIAMOND_MODEL_NAME, path="./Models/Tensorflow/Best_Dojos/", load=True, trainable = False)
zombie_net = Network(local_size=LOCAL_GRID_SIZE, name=ZOMBIE_MODEL_NAME, path="./Models/Tensorflow/Best_Dojos/", load=True, trainable = False)
explore_net = Network(local_size=LOCAL_GRID_SIZE, name=EXPLORE_MODEL_NAME, path="./Models/Tensorflow/Best_Dojos/", load=True, trainable = False)
brain = Brain(epsilon=0.0, action_space=3)
model.setup(brain)
diamond_net.setup(brain)
zombie_net.setup(brain)
explore_net.setup(brain)
avg_time = 0
avg_score = 0
avg_reward = 0
cumulative_reward = 0
# Number of episodes
print_episode = 100
total_episodes = 100
saver = tf.train.Saver()
# Initialising all variables (weights and biases)
init = tf.global_variables_initializer()
# GPU capabilities
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)
# Begin session
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
if USE_SAVED_MODEL_FILE:
saver.restore(sess, MODEL_PATH_SAVE)
print("Model restored.")
else:
sess.run(init)
start_time = time.time()
print("")
for episode in range(total_episodes):
if RANDOMIZE_MAPS:
# Make a random map 0: lava, 1: obstacle
MAP_PATH = "./Maps/Grid10/map{}.txt".format(np.random.randint(10))
env.set_map(MAP_PATH)
state, info = env.reset()
done = False
if RENDER_TO_SCREEN:
env.render()
while not done:
dojo = brain.choose_action(state, sess, model)
# print(dojo)
if dojo == 0:
dojo_state = state
# dojo_state = np.delete(dojo_state, 2, 0)# Take out the zombie layer
# dojo_state = np.delete(dojo_state, 2, 0)# Take out the history layer
action = brain.choose_dojo(dojo_state, sess, diamond_net, env.number_of_actions(), 0.0)
elif dojo == 1:
dojo_state = state
# dojo_state = np.delete(dojo_state, 1, 0)# Take out the diamond layer
# dojo_state = np.delete(dojo_state, 2, 0)# Take out the history layer
action = brain.choose_dojo(dojo_state, sess, zombie_net, env.number_of_actions(), 0.0)
elif dojo == 2:
dojo_state = state
# dojo_state = np.delete(dojo_state, 1, 0)# Take out the diamond layer
# dojo_state = np.delete(dojo_state, 1, 0)# Take out the zombie layer
action = brain.choose_dojo(dojo_state, sess, explore_net, env.number_of_actions(), 0.0)
# print(action)
# Update environment with by performing action
new_state, reward, done, info = env.step(action)
# print(new_state)
state = new_state
cumulative_reward += reward
if RENDER_TO_SCREEN:
env.render()
if done:
avg_time += info["time"]
avg_score += info["score"]
avg_reward += cumulative_reward
cumulative_reward = 0
if (episode%print_episode == 0 and episode != 0) or (episode == total_episodes-1):
current_time = math.floor(time.time()-start_time)
print("Ep:", episode,
"\tavg t: {0:.3f}".format(avg_time/print_episode),
"\tavg score: {0:.3f}".format(avg_score/print_episode),
"\tavg_reward {0:.3f}".format(avg_reward/print_episode), # avg cumulative reward
"\tepsilon {0:.3f}".format(brain.EPSILON),
end="")
print_readable_time(current_time)
avg_time = 0
avg_score = 0
avg_reward = 0
def run():
MODEL_NAME = "explore15_input6"
FOLDER = "Best_Dojos"
MODEL_PATH_SAVE = "./Models/Tensorflow/"+FOLDER+"/"+MODEL_NAME+"/"+MODEL_NAME+".ckpt"
USE_SAVED_MODEL_FILE = False
GRID_SIZE = 32
LOCAL_GRID_SIZE = 15
MAP_NUMBER = 0
RANDOMIZE_MAPS = False
# MAP_PATH = "./Maps/Grid{}/map{}.txt".format(GRID_SIZE, MAP_NUMBER)
MAP_PATH = None
MAP_PATH = "./Maps/Grid{}/impossible_map1.txt".format(GRID_SIZE, MAP_NUMBER)
print("\n ---- Running the Deep Q Network ----- \n")
RENDER_TO_SCREEN = False
RENDER_TO_SCREEN = True
env = Environment(wrap = False,
grid_size = GRID_SIZE,
local_size = LOCAL_GRID_SIZE,
rate = 80,
max_time = 60,
food_count = 0,
obstacle_count = 0,
lava_count = 0,
zombie_count = 0,
history = 40,
action_space = 5,
map_path = MAP_PATH)
if RENDER_TO_SCREEN:
env.prerender()
model = Network(local_size=LOCAL_GRID_SIZE, name=MODEL_NAME, load=True, path="./Models/Tensorflow/"+FOLDER+"/", trainable = False)
brain = Brain(epsilon=0.0, action_space = env.number_of_actions())
model.setup(brain)
avg_time = 0
avg_score = 0
avg_reward = 0
cumulative_reward = 0
# Number of episodes
print_episode = 100
total_episodes = 100
saver = tf.train.Saver()
# Initialising all variables (weights and biases)
init = tf.global_variables_initializer()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
# Begin session
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
if USE_SAVED_MODEL_FILE:
saver.restore(sess, MODEL_PATH_SAVE)
print("Model restored.")
else:
sess.run(init)
print("")
for episode in range(total_episodes):
if RANDOMIZE_MAPS:
MAP_PATH = "./Maps/Grid{}/map{}.txt".format(GRID_SIZE, np.random.randint(10))
env.set_map(MAP_PATH)
# state, info = env.reset()
state, info = env.quick_reset()
done = False
if RENDER_TO_SCREEN:
env.render()
while not done:
action = brain.choose_action(state, sess, model)
# print(action)
# Update environment with by performing action
new_state, reward, done, info = env.step(action)
# print(new_state)
state = new_state
cumulative_reward += reward
if RENDER_TO_SCREEN:
env.render()
if done:
avg_time += info["time"]
avg_score += info["score"]
avg_reward += cumulative_reward
cumulative_reward = 0
if (episode%print_episode == 0 and episode != 0) or (episode == total_episodes-1):
print("Ep:", episode,
"\tavg t: {0:.3f}".format(avg_time/print_episode),
"\tavg score: {0:.3f}".format(avg_score/print_episode),
"\tavg_reward {0:.3f}".format(avg_reward/print_episode), # avg cumulative reward
"\tepsilon {0:.3f}".format(brain.EPSILON),
end="\n")
avg_time = 0
avg_score = 0
avg_reward = 0
def train_MetaNetwork():
print("\n ---- Training the Meta Network ----- \n")
MODEL_NAME = "meta_grid16_zero_2"
MODEL_NAME_save = "meta_grid16_zero_2"
DIAMOND_MODEL_NAME = "diamond_grid16_4"
ZOMBIE_MODEL_NAME = "zombie_grid16_2"
EXPLORE_MODEL_NAME = "explore_grid16_2"
# EXTRA_MODEL_NAME = "extra15_input6_2"
# MODEL_NAME = "meta15_input6_1M_unfrozen_dojos"
# DIAMOND_MODEL_NAME = "diamond15_input4_best_unfrozen_at_1M"
# ZOMBIE_MODEL_NAME = "zombie15_input4_best_unfrozen_at_1M"
# EXPLORE_MODEL_NAME = "explore15_input4_best_unfrozen_at_1M"
# MODEL_NAME = "meta15_input6_1M_random_unfrozen_cointoss"
# DIAMOND_MODEL_NAME = "diamond15_input4_1M_random_unfrozen_cointoss"
# ZOMBIE_MODEL_NAME = "zombie15_input4_1M_random_unfrozen_cointoss"
# EXPLORE_MODEL_NAME = "explore15_input4_1M_random_unfrozen_cointoss"k
FOLDER = "Impossible"
DOJO_FOLDER = "Impossible"
MODEL_PATH_SAVE = "./Models/Tensorflow/"+FOLDER+"/"+MODEL_NAME+"/"+MODEL_NAME+".ckpt"
LOGDIR = "./Logs/"+FOLDER+"/"+MODEL_NAME_save+""
USE_SAVED_MODEL_FILE = False
GRID_SIZE = 16
LOCAL_GRID_SIZE = 15
MAP_PATH = None
RANDOMIZE_MAPS = True
RENDER_TO_SCREEN = False
# RENDER_TO_SCREEN = True
env = Environment(wrap = False,
grid_size = GRID_SIZE,
local_size = LOCAL_GRID_SIZE,
rate = 80,
max_time = 120,
food_count = 0,
obstacle_count = 0,
lava_count = 0,
zombie_count = 0,
history = 100,
action_space = 5,
map_path = MAP_PATH)
if RENDER_TO_SCREEN:
env.prerender()
model = MetaNetwork(local_size=LOCAL_GRID_SIZE, name=MODEL_NAME, path="./Models/Tensorflow/"+FOLDER+"/", load=False, trainable=True)
diamond_net = Network(local_size=LOCAL_GRID_SIZE, name=DIAMOND_MODEL_NAME, path="./Models/Tensorflow/"+DOJO_FOLDER+"/", load=True, trainable=False)
zombie_net = Network(local_size=LOCAL_GRID_SIZE, name=ZOMBIE_MODEL_NAME, path="./Models/Tensorflow/"+DOJO_FOLDER+"/", load=True, trainable=False)
explore_net = Network(local_size=LOCAL_GRID_SIZE, name=EXPLORE_MODEL_NAME, path="./Models/Tensorflow/"+DOJO_FOLDER+"/", load=True, trainable=False)
# extra_net = Network(local_size=LOCAL_GRID_SIZE, name=EXTRA_MODEL_NAME, path="./Models/Tensorflow/"+DOJO_FOLDER+"/", load=False, trainable=True)
brain = Brain(epsilon=0.05, action_space=3)
model.setup(brain)
diamond_net.setup(brain)
zombie_net.setup(brain)
explore_net.setup(brain)
# extra_net.setup(brain)
score = tf.placeholder(tf.float32, [])
avg_t = tf.placeholder(tf.float32, [])
epsilon = tf.placeholder(tf.float32, [])
avg_r = tf.placeholder(tf.float32, [])
tf.summary.scalar('error', tf.squeeze(model.error))
tf.summary.scalar('score', score)
tf.summary.scalar('average time', avg_t)
tf.summary.scalar('epsilon', epsilon)
tf.summary.scalar('avg reward', avg_r)
avg_time = 0
avg_score = 0
avg_error = 0
avg_reward = 0
cumulative_reward = 0
# Number of episodes
print_episode = 1000
total_episodes = 100000
saver = tf.train.Saver()
# Initialising all variables (weights and biases)
init = tf.global_variables_initializer()
# Adds a summary graph of the error over time
merged_summary = tf.summary.merge_all()
# Tensorboard capabilties
writer = tf.summary.FileWriter(LOGDIR)
# Histogram
histogram = Histogram(3, 10, total_episodes)
# GPU capabilities
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)
# Begin session
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
if USE_SAVED_MODEL_FILE:
saver.restore(sess, MODEL_PATH_SAVE)
print("Model restored.")
else:
sess.run(init)
writer.add_graph(sess.graph)
start_time = time.time()
print("")
for episode in range(total_episodes):
if RANDOMIZE_MAPS:
# Make a random map 0: lava, 1: obstacle
MAP_PATH = "./Maps/Grid{}/impossible_map{}.txt".format(GRID_SIZE, np.random.randint(5))
env.set_map(MAP_PATH)
# state, info = env.reset()
state, info = env.quick_reset()
done = False
# brain.linear_epsilon_decay(total_episodes, episode, start=1.0, end=0.05, percentage=0.5)
# brain.linear_alpha_decay(total_episodes, episode)
if RENDER_TO_SCREEN:
env.render()
while not done:
# Retrieve the Q values from the NN in vector form
Dojo_vector = sess.run(model.q_values, feed_dict={model.input: state})
dojo = brain.choose_action(state, sess, model)
histogram.check_section(episode)
histogram.add(dojo)
# dojo = np.random.randint(3)
# dojo = 0
# print(dojo)
if dojo == 0:
dojo_state = state
# dojo_state[2]=0
# dojo_state[3]=0
# dojo_state = np.delete(dojo_state, 2, 0)# Take out the zombie layer
# dojo_state = np.delete(dojo_state, 2, 0)# Take out the history layer
action = brain.choose_dojo(dojo_state, sess, diamond_net, env.number_of_actions(), 0.05)
elif dojo == 1:
dojo_state = state
# dojo_state[1]=0
# dojo_state[3]=0
# dojo_state = np.delete(dojo_state, 1, 0)# Take out the diamond layer
# dojo_state = np.delete(dojo_state, 2, 0)# Take out the history layer
action = brain.choose_dojo(dojo_state, sess, zombie_net, env.number_of_actions(), 0.05)
elif dojo == 2:
dojo_state = state
# dojo_state[1]=0
# dojo_state[2]=0
# dojo_state = np.delete(dojo_state, 1, 0)# Take out the diamond layer
# dojo_state = np.delete(dojo_state, 1, 0)# Take out the zombie layer
action = brain.choose_dojo(dojo_state, sess, explore_net, env.number_of_actions(), 0.05)
# elif dojo == 3:
# dojo_state = state
# action = brain.choose_dojo(dojo_state, sess, extra_net, env.number_of_actions(), 0.05)
# print(action)
# Update environment with by performing action
new_state, reward, done, info = env.step(action)
# print(new_state)
brain.store_transition_dojo(state, action, reward, done, new_state, dojo)
# print(tf.trainable_variables(scope=None))
# if dojo == 0:
# e, Q_vector = brain.train_3_dojos(diamond_net, sess, dojo)
# elif dojo == 1:
# e, Q_vector = brain.train_3_dojos(zombie_net, sess, dojo)
# elif dojo == 2:
# e, Q_vector = brain.train_3_dojos(explore_net, sess, dojo)
# e, Q_vector = brain.train_3(sess, diamond_net, zombie_net, explore_net)
# e, Q_vector = brain.train(extra_net, sess)
if done:
Dojo_vector[:,dojo] = reward
# print("Reward:", reward)
else:
# Gathering the now current state's action-value vector
y_prime = sess.run(model.q_values, feed_dict={model.input: new_state})
# Equation for training
maxq = sess.run(model.y_prime_max, feed_dict={model.actions: y_prime})
# RL Equation
Dojo_vector[:,dojo] = reward + (brain.GAMMA * maxq)
_, e = sess.run([model.optimizer, model.error], feed_dict={model.input: state, model.actions: Dojo_vector})
state = new_state
cumulative_reward += reward
if RENDER_TO_SCREEN:
env.render()
if done:
avg_time += info["time"]
avg_score += info["score"]
avg_error += e
avg_reward += cumulative_reward
cumulative_reward = 0
if (episode%print_episode == 0 and episode != 0) or (episode == total_episodes-1):
current_time = math.floor(time.time()-start_time)
print("Ep:", episode,
"\tavg t: {0:.3f}".format(avg_time/print_episode),
"\tavg score: {0:.3f}".format(avg_score/print_episode),
"\tErr {0:.3f}".format(avg_error/print_episode),
"\tavg_reward {0:.3f}".format(avg_reward/print_episode), # avg cumulative reward
"\tepsilon {0:.3f}".format(brain.EPSILON),
end="")
print_readable_time(current_time)
# Save the model's weights and biases to .npz file
model.save(sess, name=MODEL_NAME_save)
# diamond_net.save(sess, name=DIAMOND_MODEL_NAME+"")
# zombie_net.save(sess, name=ZOMBIE_MODEL_NAME+"")
# explore_net.save(sess, name=EXPLORE_MODEL_NAME+"")
# extra_net.save(sess, name=EXTRA_MODEL_NAME+"")
# save_path = saver.save(sess, MODEL_PATH_SAVE)
s = sess.run(merged_summary, feed_dict={model.input: state, model.actions: Dojo_vector, score:avg_score/print_episode, avg_t:avg_time/print_episode, epsilon:brain.EPSILON, avg_r:avg_reward/print_episode})
writer.add_summary(s, episode)
avg_time = 0
avg_score = 0
avg_error = 0
avg_reward = 0
model.save(sess, verbose=True, name=MODEL_NAME_save)
# diamond_net.save(sess, verbose=True, name=DIAMOND_MODEL_NAME+"")
# zombie_net.save(sess, verbose=True, name=ZOMBIE_MODEL_NAME+"")
# explore_net.save(sess, verbose=True, name=EXPLORE_MODEL_NAME+"")
# extra_net.save(sess, verbose=True, name=EXTRA_MODEL_NAME+"")
# save_path = saver.save(sess, MODEL_PATH_SAVE)
# print("Model saved in path: %s" % save_path)
writer.close()
histogram.plot()
local_size = LOCAL_GRID_SIZE,
rate = 80,
max_time = 120,
food_count = 20,
stick_count = 0,
obstacle_count = 0,
lava_count = 0,
zombie_count = 0,
history = 0,
action_space = 5,
map_path = MAP_PATH)
if RENDER_TO_SCREEN:
env.prerender()
model = Network(local_size=LOCAL_GRID_SIZE, name=MODEL_NAME, load=True, path="./Models/Tensorflow/"+FOLDER+"/")
brain = Brain(epsilon=0.1, action_space = env.number_of_actions())
model.setup(brain)
score = tf.placeholder(tf.float32, [])
avg_t = tf.placeholder(tf.float32, [])
epsilon = tf.placeholder(tf.float32, [])
avg_r = tf.placeholder(tf.float32, [])
tf.summary.scalar('error', tf.squeeze(model.error))
tf.summary.scalar('score', score)
tf.summary.scalar('average time', avg_t)
tf.summary.scalar('epsilon', epsilon)
tf.summary.scalar('avg reward', avg_r)
# model_type = 'dqn'
model_path = "policies/22-1-2021_13-44/policy0.tar"
env = LunarLander()
env.reset()
exit_program = False
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
if model_type == 'policy':
model = Policy(env.observation_dim, env.action_dim)
elif model_type == 'dqn':
model = Network(env.observation_dim, env.action_dim)
model.to(device)
model.load_state_dict(torch.load(model_path))
model.eval()
state = env.get_state()
while not exit_program:
env.render()
action = model(
torch.tensor(state, dtype=torch.float32,
device=device).unsqueeze(0)).argmax()
state, reward, done = env.step(action)
# Process game events
for event in pygame.event.get():
def main(env, args):
# Fix random seeds and number of threads
np.random.seed(args.seed)
tf.random.set_seed(args.seed)
tf.config.threading.set_inter_op_parallelism_threads(args.threads)
tf.config.threading.set_intra_op_parallelism_threads(args.threads)
if args.recodex:
model = load_vae()
best_params = np.load('best_params.npy', allow_pickle=True)
# TODO: Perform evaluation of a trained model.
while True:
state, done = env.reset(start_evaluation=True), False
while not done:
# env.render()
# TODO: Choose an action
action = decide_action(model, state, best_params)
state, reward, done, _ = env.step(action)
elif args.DQN:
network = Network(env, args)
if os.path.exists('dqn.model'):
network.model = tf.keras.models.load_model('dqn.model')
vae = load_vae()
replay_buffer = collections.deque(maxlen=100000)
Transition = collections.namedtuple(
"Transition", ["state", "action", "reward", "done", "next_state"])
epsilon = 0.25
gamma = 1
for i in tqdm(range(10000)):
state, done = env.reset(), False
while not done:
embedding = vae.get_latent_representation(np.array([state]))
q_values = network.predict(embedding)[0]
if np.random.uniform() >= epsilon:
action = np.argmax(q_values)
else:
action = np.random.randint(0, env.action_space.n)
next_state, reward, done, _ = env.step(action)
replay_buffer.append(
Transition(
embedding, action, reward, done,
vae.get_latent_representation(np.array([next_state]))))
if len(replay_buffer) > 32:
minibatch = random.sample(replay_buffer, 32)
states = np.array([t.state[0] for t in minibatch])
actions = np.array([t.action for t in minibatch])
rewards = np.array([t.reward for t in minibatch])
dones = np.array([t.done for t in minibatch])
next_states = np.array(
[t.next_state[0] for t in minibatch])
q_values = np.array(network.predict(states))
q_values_next = network.predict(next_states)
for Q, action, reward, next_Q, is_done in zip(
q_values, actions, rewards, q_values_next, dones):
Q[action] = reward + (0 if is_done else gamma *
np.max(next_Q))
network.train(states, q_values)
if i % 100 == 0:
network.update_target_weights()
if i % 100 == 0:
network.save()
state = next_state
epsilon = np.exp(
np.interp(env.episode + 1, [0, 5000],
[np.log(0.25), np.log(0.01)]))
elif args.evolution:
es = train(load_from='saved_model.pkl')
np.save('best_params', es.best.get()[0])
best_params = es.best.get()[0]
play(best_params, render=True)
def train():
MODEL_NAME = "diamond9_input5"
MODEL_NAME_save = "diamond9_input5"
FOLDER = "Best_Dojos9"
MODEL_PATH_SAVE = "./Models/Tensorflow/" + FOLDER + "/" + MODEL_NAME_save + "/" + MODEL_NAME_save + ".ckpt"
LOGDIR = "./Logs/" + FOLDER + "/" + MODEL_NAME_save + "_2"
USE_SAVED_MODEL_FILE = False
GRID_SIZE = 8
LOCAL_GRID_SIZE = 9
MAP_NUMBER = 0
RANDOMIZE_MAPS = False
# MAP_PATH = "./Maps/Grid{}/map{}.txt".format(GRID_SIZE, MAP_NUMBER)
MAP_PATH = None
print("\n ---- Training the Deep Neural Network ----- \n")
RENDER_TO_SCREEN = False
# RENDER_TO_SCREEN = True
env = Environment(wrap=False,
grid_size=GRID_SIZE,
local_size=LOCAL_GRID_SIZE,
rate=80,
max_time=50,
food_count=10,
obstacle_count=0,
lava_count=0,
zombie_count=0,
history=0,
action_space=5,
map_path=MAP_PATH)
if RENDER_TO_SCREEN:
env.prerender()
model = Network(local_size=LOCAL_GRID_SIZE,
name=MODEL_NAME,
load=False,
path="./Models/Tensorflow/" + FOLDER + "/")
brain = Brain(epsilon=0.1, action_space=env.number_of_actions())
model.setup(brain)
score = tf.placeholder(tf.float32, [])
avg_t = tf.placeholder(tf.float32, [])
epsilon = tf.placeholder(tf.float32, [])
avg_r = tf.placeholder(tf.float32, [])
tf.summary.scalar('error', tf.squeeze(model.error))
tf.summary.scalar('score', score)
tf.summary.scalar('average time', avg_t)
tf.summary.scalar('epsilon', epsilon)
tf.summary.scalar('avg reward', avg_r)
avg_time = 0
avg_score = 0
avg_error = 0
avg_reward = 0
cumulative_reward = 0
# Number of episodes
print_episode = 100
total_episodes = 10000
saver = tf.train.Saver()
# Initialising all variables (weights and biases)
init = tf.global_variables_initializer()
# Adds a summary graph of the error over time
merged_summary = tf.summary.merge_all()
# Tensorboard capabilties
writer = tf.summary.FileWriter(LOGDIR)
# Assume that you have 12GB of GPU memory and want to allocate ~4GB:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
# Begin session
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
if USE_SAVED_MODEL_FILE:
saver.restore(sess, MODEL_PATH_SAVE)
print("Model restored.")
else:
sess.run(init)
# for episode in range(50):
# state, info = env.reset()
# done = False
# if RENDER_TO_SCREEN:
# env.render()
# while not done:
# action = brain.choose_action(state, sess, model)
# new_state, reward, done, info = env.step(action)
# brain.store_transition(state, action, reward, done, new_state)
# state = new_state
# if RENDER_TO_SCREEN:
# env.render()
# print("\nREPLAY MEMORY INITIALISED")
# print(brain.memCntr)
writer.add_graph(sess.graph)
start_time = time.time()
print("")
for episode in range(total_episodes):
if RANDOMIZE_MAPS:
MAP_PATH = "./Maps/Grid10/map{}.txt".format(
np.random.randint(10))
env.set_map(MAP_PATH)
state, info = env.reset()
done = False
# brain.linear_epsilon_decay(total_episodes, episode, start=0.4, end=0.05, percentage=0.8)
# brain.linear_alpha_decay(total_episodes, episode)
if RENDER_TO_SCREEN:
env.render()
while not done:
action = brain.choose_action(state, sess, model)
# print(action)
# Update environment by performing action
new_state, reward, done, info = env.step(action)
# print(new_state)
brain.store_transition(state, action, reward, done, new_state)
# e, Q_vector = brain.train_batch(4, model, sess)
e, Q_vector = brain.train(model, sess)
state = new_state
cumulative_reward += reward
if RENDER_TO_SCREEN:
env.render()
if done:
avg_time += info["time"]
avg_score += info["score"]
avg_error += e
avg_reward += cumulative_reward
cumulative_reward = 0
if (episode % print_episode == 0
and episode != 0) or (episode == total_episodes - 1):
current_time = math.floor(time.time() - start_time)
print(
"Ep:",
episode,
"\tavg t: {0:.3f}".format(avg_time / print_episode),
"\tavg score: {0:.3f}".format(avg_score / print_episode),
"\tErr {0:.3f}".format(avg_error / print_episode),
"\tavg_reward {0:.3f}".format(
avg_reward / print_episode), # avg cumulative reward
"\tepsilon {0:.3f}".format(brain.EPSILON),
end="")
print_readable_time(current_time)
# Save the model's weights and biases to .npz file
model.save(sess, name=MODEL_NAME_save)
# save_path = saver.save(sess, MODEL_PATH_SAVE)
s = sess.run(merged_summary,
feed_dict={
model.input: state,
model.actions: Q_vector,
score: avg_score / print_episode,
avg_t: avg_time / print_episode,
epsilon: brain.EPSILON,
avg_r: avg_reward / print_episode
})
writer.add_summary(s, episode)
avg_time = 0
avg_score = 0
avg_error = 0
avg_reward = 0
model.save(sess, verbose=True, name=MODEL_NAME_save)
# save_path = saver.save(sess, MODEL_PATH_SAVE)
# print("Model saved in path: %s" % save_path)
writer.close()
def train():
MODEL_NAME = "diamond_local15_maps"
MODEL_PATH_SAVE = "./Models/Tensorflow/Maps/" + MODEL_NAME + "/" + MODEL_NAME + ".ckpt"
LOGDIR = "./Logs/" + MODEL_NAME
USE_SAVED_MODEL_FILE = False
GRID_SIZE = 10
LOCAL_GRID_SIZE = 15
MAP_NUMBER = 0
RANDOMIZE_MAPS = True
# MAP_PATH = "./Maps/Grid{}/map{}.txt".format(GRID_SIZE, MAP_NUMBER)
MAP_PATH = None
print("\n ---- Training the Deep Neural Network ----- \n")
RENDER_TO_SCREEN = False
RENDER_TO_SCREEN = True
env = Environment(wrap=False,
grid_size=GRID_SIZE,
local_size=LOCAL_GRID_SIZE,
rate=80,
max_time=50,
food_count=3,
obstacle_count=1,
lava_count=1,
zombie_count=0,
action_space=5,
map_path=MAP_PATH)
if RENDER_TO_SCREEN:
env.prerender()
model = Network(local_size=LOCAL_GRID_SIZE,
name=MODEL_NAME,
load=False,
path="./Models/Tensorflow/Maps/")
brain = Brain(epsilon=0.05, action_space=env.number_of_actions())
model.setup(brain)
tf.summary.scalar('error', tf.squeeze(model.error))
avg_time = 0
avg_score = 0
avg_error = 0
# Number of episodes
print_episode = 1000
total_episodes = 100000
saver = tf.train.Saver()
# Initialising all variables (weights and biases)
init = tf.global_variables_initializer()
# Adds a summary graph of the error over time
merged_summary = tf.summary.merge_all()
# Tensorboard capabilties
# writer = tf.summary.FileWriter(LOGDIR)
# Assume that you have 12GB of GPU memory and want to allocate ~4GB:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)
# Begin session
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
if USE_SAVED_MODEL_FILE:
saver.restore(sess, MODEL_PATH_SAVE)
print("Model restored.")
sess.run(init)
# writer.add_graph(sess.graph)
start_time = time.time()
print("")
for episode in range(total_episodes):
if RANDOMIZE_MAPS:
# Make a random map 0: lava, 1: obstacle
MAP_PATH = "./Maps/Grid10/map{}.txt".format(
np.random.randint(10))
env.set_map(MAP_PATH)
state, info = env.reset()
done = False
brain.linear_epsilon_decay(total_episodes,
episode,
start=0.5,
end=0.05,
percentage=0.6)
# brain.linear_alpha_decay(total_episodes, episode)
if RENDER_TO_SCREEN:
env.render()
while not done:
# Retrieve the Q values from the NN in vector form
# Q_vector = sess.run(model.q_values, feed_dict={model.input: state})
action = brain.choose_action(state, sess, model)
# print(action)
# Update environment by performing action
new_state, reward, done, info = env.step(action)
# print(new_state)
brain.store_transition(state, action, reward, done, new_state)
e = brain.train(model, sess)
state = new_state
if RENDER_TO_SCREEN:
env.render()
if done:
avg_time += info["time"]
avg_score += info["score"]
avg_error += e
if (episode % print_episode == 0
and episode != 0) or (episode == total_episodes - 1):
current_time = math.floor(time.time() - start_time)
print("Ep:",
episode,
"\tavg t: {0:.3f}".format(avg_time / print_episode),
"\tavg score: {0:.3f}".format(avg_score / print_episode),
"\tErr {0:.3f}".format(avg_error / print_episode),
"\tepsilon {0:.3f}".format(brain.EPSILON),
end="")
print_readable_time(current_time)
avg_time = 0
avg_score = 0
avg_error = 0
# Save the model's weights and biases to .npz file
model.save(sess)
save_path = saver.save(sess, MODEL_PATH_SAVE)
# s = sess.run(merged_summary, feed_dict={model.input: state, model.actions: Q_vector})
# writer.add_summary(s, episode)
model.save(sess, verbose=True)
save_path = saver.save(sess, MODEL_PATH_SAVE)
print("Model saved in path: %s" % save_path)
def train_MetaNetwork():
print("\n ---- Training the Meta Network ----- \n")
MODEL_NAME = "meta_network_local15"
DIAMOND_MODEL_NAME = "diamond_dojo_local15"
ZOMBIE_MODEL_NAME = "zombie_dojo_local15"
# EXPLORE_MODEL_NAME = "explore_dojo_local15"
MODEL_PATH_SAVE = "./Models/Tensorflow/" + MODEL_NAME + "/" + MODEL_NAME + ".ckpt"
LOGDIR = "./Logs/" + MODEL_NAME
USE_SAVED_MODEL_FILE = False
GRID_SIZE = 8
LOCAL_GRID_SIZE = 15
MAP_PATH = None
RENDER_TO_SCREEN = False
# RENDER_TO_SCREEN = True
env = Environment(wrap=False,
grid_size=GRID_SIZE,
local_size=LOCAL_GRID_SIZE,
rate=80,
max_time=200,
food_count=3,
obstacle_count=0,
lava_count=0,
zombie_count=1,
action_space=5,
map_path=MAP_PATH)
if RENDER_TO_SCREEN:
env.prerender()
model = MetaNetwork(local_size=LOCAL_GRID_SIZE, name=MODEL_NAME, load=True)
diamond_net = Network(local_size=LOCAL_GRID_SIZE,
name=DIAMOND_MODEL_NAME,
load=True,
trainable=False)
zombie_net = Network(local_size=LOCAL_GRID_SIZE,
name=ZOMBIE_MODEL_NAME,
load=True,
trainable=False)
# explore_net = Network(local_size=LOCAL_GRID_SIZE, name=EXPLORE_MODEL_NAME, load=True, trainable = False)
brain = Brain(epsilon=0.01, action_space=2)
model.setup(brain)
diamond_net.setup(brain)
zombie_net.setup(brain)
# explore_net.setup(brain)
tf.summary.scalar('error', tf.squeeze(model.error))
avg_time = 0
avg_score = 0
avg_error = 0
# Number of episodes
print_episode = 1000
total_episodes = 100000
saver = tf.train.Saver()
# Initialising all variables (weights and biases)
init = tf.global_variables_initializer()
# Adds a summary graph of the error over time
merged_summary = tf.summary.merge_all()
# Tensorboard capabilties
writer = tf.summary.FileWriter(LOGDIR)
# GPU capabilities
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.4)
# Begin session
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
if USE_SAVED_MODEL_FILE:
saver.restore(sess, MODEL_PATH_SAVE)
print("Model restored.")
else:
sess.run(init)
writer.add_graph(sess.graph)
start_time = time.time()
print("")
for episode in range(total_episodes):
state, info = env.reset()
done = False
brain.linear_epsilon_decay(total_episodes,
episode,
start=0.3,
end=0.02,
percentage=0.5)
# brain.linear_alpha_decay(total_episodes, episode)
if RENDER_TO_SCREEN:
env.render()
while not done:
# Retrieve the Q values from the NN in vector form
Dojo_vector = sess.run(model.q_values,
feed_dict={model.input: state})
dojo = brain.choose_action(state, sess, model)
# print(dojo)
if dojo == 0:
# state[2] = 0 # Zero out the zombies layer
state = np.delete(state, 2, 0) # Take out the zombie layer
state = np.delete(state, 5,
0) # Take out the history layer
action = brain.choose_dojo(state, sess, diamond_net,
env.number_of_actions(), 0.01)
elif dojo == 1:
# state[1] = 0 # Zero out the diamond layer
state = np.delete(state, 1,
0) # Take out the diamond layer
state = np.delete(state, 5,
0) # Take out the history layer
action = brain.choose_dojo(state, sess, zombie_net,
env.number_of_actions(), 0.01)
elif dojo == 2:
state = np.delete(state, 1,
0) # Take out the diamond layer
state = np.delete(state, 2, 0) # Take out the zombie layer
action = brain.choose_dojo(state, sess, explore_net,
env.number_of_actions(), 0.01)
# print(action)
# Update environment with by performing action
new_state, reward, done, info = env.step(action)
# print(new_state)
brain.store_transition(state, dojo, reward, done, new_state)
## Standard training with learning after every step
# print(tf.trainable_variables(scope=None))
if done:
Dojo_vector[:, dojo] = reward
# print("Reward:", reward)
else:
# Gathering the now current state's action-value vector
y_prime = sess.run(model.q_values,
feed_dict={model.input: new_state})
# Equation for training
maxq = sess.run(model.y_prime_max,
feed_dict={model.actions: y_prime})
# RL Equation
Dojo_vector[:, dojo] = reward + (brain.GAMMA * maxq)
_, e = sess.run([model.optimizer, model.error],
feed_dict={
model.input: state,
model.actions: Dojo_vector
})
## Training using replay memory
state = new_state
if RENDER_TO_SCREEN:
env.render()
if done:
avg_time += info["time"]
avg_score += info["score"]
avg_error += e
if (episode % print_episode == 0
and episode != 0) or (episode == total_episodes - 1):
current_time = math.floor(time.time() - start_time)
print("Ep:",
episode,
"\tavg t: {0:.3f}".format(avg_time / print_episode),
"\tavg score: {0:.3f}".format(avg_score / print_episode),
"\tErr {0:.3f}".format(avg_error / print_episode),
"\tepsilon {0:.3f}".format(brain.EPSILON),
end="")
print_readable_time(current_time)
avg_time = 0
avg_score = 0
avg_error = 0
# Save the model's weights and biases to .npz file
model.save(sess)
save_path = saver.save(sess, MODEL_PATH_SAVE)
s = sess.run(merged_summary,
feed_dict={
model.input: state,
model.actions: Dojo_vector
})
writer.add_summary(s, episode)
model.save(sess, verbose=True)
save_path = saver.save(sess, MODEL_PATH_SAVE)
print("Model saved in path: %s" % save_path)
writer.close()
