class UCT():
def __init__(self,
env_params,
max_steps=1000,
max_depth=20,
max_width=5,
gamma=1.0,
policy="Random",
seed=123,
device=torch.device("cpu")):
self.env_params = env_params
self.max_steps = max_steps
self.max_depth = max_depth
self.max_width = max_width
self.gamma = gamma
self.policy = policy
self.seed = seed
self.device = device
self.policy_wrapper = None
# Environment
self.wrapped_env = EnvWrapper(**env_params)
# Environment properties
self.action_n = self.wrapped_env.get_action_n()
self.max_width = min(self.action_n, self.max_width)
assert self.max_depth > 0 and 0 < self.max_width <= self.action_n
# Checkpoint data manager
self.checkpoint_data_manager = CheckpointManager()
self.checkpoint_data_manager.hock_env("main", self.wrapped_env)
# For MCTS tree
self.root_node = None
self.global_saving_idx = 0
self.init_policy()
def init_policy(self):
self.policy_wrapper = PolicyWrapper(self.policy,
self.env_params["env_name"],
self.action_n, self.device)
# Entrance of the P-UCT algorithm
def simulate_trajectory(self, max_episode_length=-1):
state = self.wrapped_env.reset()
accu_reward = 0.0
done = False
step_count = 0
rewards = []
times = []
game_start_time = time.time()
while not done and (max_episode_length == -1
or step_count < max_episode_length):
simulation_start_time = time.time()
action = self.simulate_single_move(state)
simulation_end_time = time.time()
next_state, reward, done = self.wrapped_env.step(action)
rewards.append(reward)
times.append(simulation_end_time - simulation_start_time)
print(
"> Time step {}, take action {}, instance reward {}, cumulative reward {}, used {} seconds"
.format(step_count, action, reward, accu_reward + reward,
simulation_end_time - simulation_start_time))
accu_reward += reward
state = next_state
step_count += 1
game_end_time = time.time()
print("> game ended. total reward: {}, used time {} s".format(
accu_reward, game_end_time - game_start_time))
return accu_reward, np.array(rewards, dtype=np.float32), np.array(
times, dtype=np.float32)
def simulate_single_move(self, state):
# Clear cache
self.root_node = None
self.global_saving_idx = 0
self.checkpoint_data_manager.clear()
gc.collect()
# Construct root node
self.checkpoint_data_manager.checkpoint_env("main",
self.global_saving_idx)
self.root_node = UCTnode(action_n=self.action_n,
state=state,
checkpoint_idx=self.global_saving_idx,
parent=None,
tree=self,
is_head=True)
self.global_saving_idx += 1
for _ in range(self.max_steps):
self.simulate_single_step()
best_action = self.root_node.max_utility_action()
self.checkpoint_data_manager.load_checkpoint_env(
"main", self.root_node.checkpoint_idx)
return best_action
def simulate_single_step(self):
# Go into root node
curr_node = self.root_node
# Selection
curr_depth = 1
while True:
if curr_node.no_child_available() or (not curr_node.all_child_visited() and
curr_node != self.root_node and np.random.random() < 0.5) or \
(not curr_node.all_child_visited() and curr_node == self.root_node):
# If no child node has been updated, we have to perform expansion anyway.
# Or if root node is not fully visited.
# Or if non-root node is not fully visited and {with prob 1/2}.
need_expansion = True
break
else:
action = curr_node.select_action()
curr_node.update_history(action, curr_node.rewards[action])
if curr_node.dones[action] or curr_depth >= self.max_depth:
need_expansion = False
break
next_node = curr_node.children[action]
curr_depth += 1
curr_node = next_node
# Expansion
if need_expansion:
expand_action = curr_node.select_expand_action()
self.checkpoint_data_manager.load_checkpoint_env(
"main", curr_node.checkpoint_idx)
next_state, reward, done = self.wrapped_env.step(expand_action)
self.checkpoint_data_manager.checkpoint_env(
"main", self.global_saving_idx)
curr_node.rewards[expand_action] = reward
curr_node.dones[expand_action] = done
curr_node.update_history(action_taken=expand_action, reward=reward)
curr_node.add_child(expand_action,
next_state,
self.global_saving_idx,
prior_prob=self.get_prior_prob(next_state))
self.global_saving_idx += 1
else:
self.checkpoint_data_manager.load_checkpoint_env(
"main", curr_node.checkpoint_idx)
next_state, reward, done = self.wrapped_env.step(action)
curr_node.rewards[action] = reward
curr_node.dones[action] = done
# Simulation
done = False
accu_reward = 0.0
accu_gamma = 1.0
while not done:
action = self.get_action(next_state)
next_state, reward, done = self.wrapped_env.step(action)
accu_reward += reward * accu_gamma
accu_gamma *= self.gamma
# Complete Update
self.complete_update(curr_node, self.root_node, accu_reward)
def get_action(self, state):
return self.policy_wrapper.get_action(state)
def get_prior_prob(self, state):
return self.policy_wrapper.get_prior_prob(state)
def close(self):
pass
@staticmethod
def complete_update(curr_node, curr_node_head, accu_reward):
while curr_node != curr_node_head:
accu_reward = curr_node.update(accu_reward)
curr_node = curr_node.parent
curr_node_head.update(accu_reward)
class WU_UCT():
def __init__(self,
env_params,
max_steps=1000,
max_depth=20,
max_width=5,
gamma=1.0,
expansion_worker_num=16,
simulation_worker_num=16,
policy="Random",
seed=123,
device="cpu",
record_video=False):
self.env_params = env_params
self.max_steps = max_steps
self.max_depth = max_depth
self.max_width = max_width
self.gamma = gamma
self.expansion_worker_num = expansion_worker_num
self.simulation_worker_num = simulation_worker_num
self.policy = policy
self.device = device
self.record_video = record_video
# Environment
record_path = "Records/P-UCT_" + env_params["env_name"] + ".mp4"
self.wrapped_env = EnvWrapper(**env_params,
enable_record=record_video,
record_path=record_path)
# Environment properties
self.action_n = self.wrapped_env.get_action_n()
self.max_width = min(self.action_n, self.max_width)
assert self.max_depth > 0 and 0 < self.max_width <= self.action_n
# Expansion worker pool
self.expansion_worker_pool = PoolManager(
worker_num=expansion_worker_num,
env_params=env_params,
policy=policy,
gamma=gamma,
seed=seed,
device=device,
need_policy=False)
# Simulation worker pool
self.simulation_worker_pool = PoolManager(
worker_num=simulation_worker_num,
env_params=env_params,
policy=policy,
gamma=gamma,
seed=seed,
device=device,
need_policy=True)
# Checkpoint data manager
self.checkpoint_data_manager = CheckpointManager()
self.checkpoint_data_manager.hock_env("main", self.wrapped_env)
# For MCTS tree
self.root_node = None
self.global_saving_idx = 0
# Task recorder
self.expansion_task_recorder = dict()
self.unscheduled_expansion_tasks = list()
self.simulation_task_recorder = dict()
self.unscheduled_simulation_tasks = list()
# Simulation count
self.simulation_count = 0
# Logging
logging.basicConfig(filename="Logs/P-UCT_" +
self.env_params["env_name"] + "_" +
str(self.simulation_worker_num) + ".log",
level=logging.INFO)
# Entrance of the P-UCT algorithm
# This is the outer loop of P-UCT simulation, where the P-UCT agent consecutively plan a best action and
# interact with the environment.
def simulate_trajectory(self, max_episode_length=-1):
state = self.wrapped_env.reset()
accu_reward = 0.0
done = False
step_count = 0
rewards = []
times = []
game_start_time = time.clock()
logging.info("Start simulation")
while not done and (max_episode_length == -1
or step_count < max_episode_length):
# Plan a best action under the current state
simulation_start_time = time.clock()
action = self.simulate_single_move(state)
simulation_end_time = time.clock()
# Interact with the environment
next_state, reward, done = self.wrapped_env.step(action)
rewards.append(reward)
times.append(simulation_end_time - simulation_start_time)
print(
"> Time step {}, take action {}, instance reward {}, cumulative reward {}, used {} seconds"
.format(step_count, action, reward, accu_reward + reward,
simulation_end_time - simulation_start_time))
logging.info(
"> Time step {}, take action {}, instance reward {}, cumulative reward {}, used {} seconds"
.format(step_count, action, reward, accu_reward + reward,
simulation_end_time - simulation_start_time))
# Record video
if self.record_video:
self.wrapped_env.capture_frame()
self.wrapped_env.store_video_files()
# update game status
accu_reward += reward
state = next_state
step_count += 1
game_end_time = time.clock()
print("> game ended. total reward: {}, used time {} s".format(
accu_reward, game_end_time - game_start_time))
logging.info("> game ended. total reward: {}, used time {} s".format(
accu_reward, game_end_time - game_start_time))
return accu_reward, np.array(rewards, dtype=np.float32), np.array(
times, dtype=np.float32)
# This is the planning process of P-UCT. Starts from a tree with a root node only,
# P-UCT performs selection, expansion, simulation, and backpropagation on it.
def simulate_single_move(self, state):
# Clear cache
self.root_node = None
self.global_saving_idx = 0
self.checkpoint_data_manager.clear()
# Clear recorders
self.expansion_task_recorder.clear()
self.unscheduled_expansion_tasks.clear()
self.simulation_task_recorder.clear()
self.unscheduled_simulation_tasks.clear()
gc.collect()
# Free all workers
self.expansion_worker_pool.wait_until_all_envs_idle()
self.simulation_worker_pool.wait_until_all_envs_idle()
# Construct root node
self.checkpoint_data_manager.checkpoint_env("main",
self.global_saving_idx)
self.root_node = WU_UCTnode(action_n=self.action_n,
state=state,
checkpoint_idx=self.global_saving_idx,
parent=None,
tree=self,
is_head=True)
# An index used to retrieve game-states
self.global_saving_idx += 1
# t_complete in the origin paper, measures the completed number of simulations
self.simulation_count = 0
# Repeatedly invoke the master loop (Figure 2 of the paper)
sim_idx = 0
while self.simulation_count < self.max_steps:
self.simulate_single_step(sim_idx)
sim_idx += 1
# Select the best root action
best_action = self.root_node.max_utility_action()
# Retrieve the game-state before simulation begins
self.checkpoint_data_manager.load_checkpoint_env(
"main", self.root_node.checkpoint_idx)
return best_action
def simulate_single_step(self, sim_idx):
# Go into root node
curr_node = self.root_node
# Selection
curr_depth = 1
while True:
if curr_node.no_child_available() or (not curr_node.all_child_visited() and
curr_node != self.root_node and np.random.random() < 0.5) or \
(not curr_node.all_child_visited() and curr_node == self.root_node):
# If no child node has been updated, we have to perform expansion anyway.
# Or if root node is not fully visited.
# Or if non-root node is not fully visited and {with prob 1/2}.
cloned_curr_node = curr_node.shallow_clone()
checkpoint_data = self.checkpoint_data_manager.retrieve(
curr_node.checkpoint_idx)
# Record the task
self.expansion_task_recorder[sim_idx] = (checkpoint_data,
cloned_curr_node,
curr_node)
self.unscheduled_expansion_tasks.append(sim_idx)
need_expansion = True
break
else:
action = curr_node.select_action()
curr_node.update_history(sim_idx, action,
curr_node.rewards[action])
if curr_node.dones[action] or curr_depth >= self.max_depth:
# Exceed maximum depth
need_expansion = False
break
if curr_node.children[action] is None:
need_expansion = False
break
next_node = curr_node.children[action]
curr_depth += 1
curr_node = next_node
# Expansion
if not need_expansion:
if not curr_node.dones[action]:
# Reach maximum depth but have not terminate.
# Record simulation task.
self.simulation_task_recorder[sim_idx] = (
action, curr_node, curr_node.checkpoint_idx, None)
self.unscheduled_simulation_tasks.append(sim_idx)
else:
# Reach terminal node.
# In this case, update directly.
self.incomplete_update(curr_node, self.root_node, sim_idx)
self.complete_update(curr_node, self.root_node, 0.0, sim_idx)
self.simulation_count += 1
else:
# Assign tasks to idle server
while len(self.unscheduled_expansion_tasks
) > 0 and self.expansion_worker_pool.has_idle_server():
# Get a task
curr_idx = np.random.randint(
0, len(self.unscheduled_expansion_tasks))
task_idx = self.unscheduled_expansion_tasks.pop(curr_idx)
# Assign the task to server
checkpoint_data, cloned_curr_node, _ = self.expansion_task_recorder[
task_idx]
self.expansion_worker_pool.assign_expansion_task(
checkpoint_data, cloned_curr_node, self.global_saving_idx,
task_idx)
self.global_saving_idx += 1
# Wait for an expansion task to complete
if self.expansion_worker_pool.server_occupied_rate() >= 0.99:
expand_action, next_state, reward, done, checkpoint_data, \
saving_idx, task_idx = self.expansion_worker_pool.get_complete_expansion_task()
curr_node = self.expansion_task_recorder.pop(task_idx)[2]
curr_node.update_history(task_idx, expand_action, reward)
# Record info
curr_node.dones[expand_action] = done
curr_node.rewards[expand_action] = reward
if done:
# If this expansion result in a terminal node, perform update directly.
# (simulation is not needed)
self.incomplete_update(curr_node, self.root_node, task_idx)
self.complete_update(curr_node, self.root_node, 0.0,
task_idx)
self.simulation_count += 1
else:
# Schedule the task to the simulation task buffer.
self.checkpoint_data_manager.store(saving_idx,
checkpoint_data)
self.simulation_task_recorder[task_idx] = (
expand_action, curr_node, saving_idx,
deepcopy(next_state))
self.unscheduled_simulation_tasks.append(task_idx)
# Assign simulation tasks to idle environment server
while len(self.unscheduled_simulation_tasks
) > 0 and self.simulation_worker_pool.has_idle_server():
# Get a task
idx = np.random.randint(0, len(self.unscheduled_simulation_tasks))
task_idx = self.unscheduled_simulation_tasks.pop(idx)
checkpoint_data = self.checkpoint_data_manager.retrieve(
self.simulation_task_recorder[task_idx][2])
first_aciton = None if self.simulation_task_recorder[task_idx][3] \
is not None else self.simulation_task_recorder[task_idx][0]
# Assign the task to server
self.simulation_worker_pool.assign_simulation_task(
task_idx, checkpoint_data, first_action=first_aciton)
# Perform incomplete update
self.incomplete_update(
self.simulation_task_recorder[task_idx]
[1], # This is the corresponding node
self.root_node,
task_idx)
# Wait for a simulation task to complete
if self.simulation_worker_pool.server_occupied_rate() >= 0.99:
args = self.simulation_worker_pool.get_complete_simulation_task()
if len(args) == 3:
task_idx, accu_reward, prior_prob = args
else:
task_idx, accu_reward, reward, done = args
expand_action, curr_node, saving_idx, next_state = self.simulation_task_recorder.pop(
task_idx)
if len(args) == 4:
curr_node.rewards[expand_action] = reward
curr_node.dones[expand_action] = done
# Add node
if next_state is not None:
curr_node.add_child(expand_action,
next_state,
saving_idx,
prior_prob=prior_prob)
# Complete Update
self.complete_update(curr_node, self.root_node, accu_reward,
task_idx)
self.simulation_count += 1
def close(self):
# Free sub-processes
self.expansion_worker_pool.close_pool()
self.simulation_worker_pool.close_pool()
# Incomplete update allows to track unobserved samples (Algorithm 2 in the paper)
@staticmethod
def incomplete_update(curr_node, curr_node_head, idx):
while curr_node != curr_node_head:
curr_node.update_incomplete(idx)
curr_node = curr_node.parent
curr_node_head.update_incomplete(idx)
# Complete update tracks the observed samples (Algorithm 3 in the paper)
@staticmethod
def complete_update(curr_node, curr_node_head, accu_reward, idx):
while curr_node != curr_node_head:
accu_reward = curr_node.update_complete(idx, accu_reward)
curr_node = curr_node.parent
curr_node_head.update_complete(idx, accu_reward)