def run_model(game_count=1):
"""
run model for game_count games
"""
# Make environment
env = WhaleEnv(
config={
'active_player': 0,
'seed': datetime.utcnow().microsecond,
'env_num': 1,
'num_players': 5
})
# Set up agents
action_num = 3
agent = SimpleAgent(action_num=action_num, player_num=5)
agent_0 = RandomAgent(action_num=action_num)
agent_1 = RandomAgent(action_num=action_num)
agent_2 = RandomAgent(action_num=action_num)
agent_3 = RandomAgent(action_num=action_num)
agents = [agent, agent_0, agent_1, agent_2, agent_3]
env.set_agents(agents)
agent.load_pretrained()
for game in range(game_count):
# Generate data from the environment
trajectories = env.run(is_training=False)
# Print out the trajectories
print('\nEpisode {}'.format(game))
i = 0
for trajectory in trajectories:
print('\tPlayer {}'.format(i))
[print(t) for t in trajectory]
i += 1
def experiment():
np.random.seed(3)
# MDP
mdp = generate_simple_chain(state_n=5,
goal_states=[2],
prob=.8,
rew=1,
gamma=.9)
action_space = mdp._mdp_info.action_space
observation_space = mdp._mdp_info.observation_space
gamma = mdp._mdp_info.gamma
# Model Block
model_block = MBlock(env=mdp, render=False)
#Policy
epsilon = Parameter(value=1)
pi = EpsGreedy(epsilon=epsilon)
table = Table(mdp.info.size)
pi.set_q(table)
#Agents
mdp_info_agent1 = MDPInfo(observation_space=observation_space,
action_space=spaces.Discrete(5),
gamma=1,
horizon=20)
mdp_info_agent2 = MDPInfo(observation_space=spaces.Discrete(5),
action_space=action_space,
gamma=gamma,
horizon=10)
agent1 = SimpleAgent(name='HIGH', mdp_info=mdp_info_agent1, policy=pi)
agent2 = SimpleAgent(name='LOW', mdp_info=mdp_info_agent2, policy=pi)
# Control Blocks
control_block1 = ControlBlock(wake_time=10,
agent=agent1,
n_eps_per_fit=None,
n_steps_per_fit=1)
control_block2 = ControlBlock(wake_time=1,
agent=agent2,
n_eps_per_fit=None,
n_steps_per_fit=1)
# Algorithm
blocks = [model_block, control_block1, control_block2]
order = [0, 1, 2]
model_block.add_input(control_block2)
control_block1.add_input(model_block)
control_block1.add_reward(model_block)
control_block2.add_input(control_block1)
control_block2.add_reward(model_block)
computational_graph = ComputationalGraph(blocks=blocks, order=order)
core = HierarchicalCore(computational_graph)
# Train
core.learn(n_steps=40, quiet=True)
return
def a_vs_b(ship_a, ship_b, trials, attack_range):
"""This function calculates the average time to destruction when a shoots at b.
Args:
ship_a ((Ship, str)): Attacker and hull zone tuple.
ship_b ((Ship, str)): Defender and hull zone tuple.
trials (int): Number of trials in average calculation.
range (str): Attack range.
"""
roll_counts = []
agent = SimpleAgent()
for trial in range(trials):
# Reset ship b for each trial
ship_b.reset()
world_state = WorldState()
world_state.addShip(ship_a, 0)
world_state.addShip(ship_b, 1)
num_rolls = 0
while ship_b.damage_cards() < ship_b.hull():
num_rolls += 1
# Handle the attack and receive the updated world state
world_state = handleAttack(world_state=world_state,
attacker=(ship_a, "front"),
defender=(ship_b, "front"),
attack_range=attack_range,
offensive_agent=agent,
defensive_agent=agent)
roll_counts.append(num_rolls)
np_counts = numpy.array(roll_counts)
return np_counts.mean()
def test3():
env = ConnectFourEnv(display=True)
simpleAgent = SimpleAgent(env, 2, 1)
state, gameOver, winner = env.act(1, 0)
state, gameOver, winner = env.act(2, 6)
time.sleep(0.5)
state, gameOver, winner = env.act(1, 1)
state, gameOver, winner = env.act(2, 5)
time.sleep(0.5)
state, gameOver, winner = env.act(1, 3)
acttion = simpleAgent.getAction(env.state)
state, gameOver, winner = env.act(2, acttion)
time.sleep(5)
def __init__(self):
# Only four navigation actions for subproblem actions
nA = 4
# Only taxi row, column and destination index for subproblem states
states_shape = (5, 5, 4)
self.sub_agent = SimpleAgent(states_shape=states_shape, nA=nA)
# Learning rate / step size
self.sub_agent.alpha = 0.01
self.sub_agent.alpha_decay = 1
self.sub_agent.alpha_min = 0
# Discount
self.sub_agent.gamma = 1
self.sub_agent.gamma_decay = 1
self.sub_agent.gamma_min = 0
# Exploration
self.sub_agent.epsilon = 0.01
self.sub_agent.epsilon_decay = 1
self.sub_agent.epsilon_min = 0
# For our params, just mimic the sub-agent's
(self.alpha, self.epsilon, self.gamma) = \
self.sub_agent.alpha, self.sub_agent.epsilon, self.sub_agent.gamma
# Environment priors
self.action_pickup = 4
self.action_dropoff = 5
self.locs = [(0, 0), (0, 4), (4, 0), (4, 3)]
self.passenger_in_taxi_idx = 4
print("alpha: {0}, alpha_decay: {1}, alpha_min: {2}".format(
self.sub_agent.alpha, self.sub_agent.alpha_decay, self.sub_agent.alpha_min))
print("gamma: {0}, gamma_decay: {1}, gamma_min: {2}".format(
self.sub_agent.gamma, self.sub_agent.gamma_decay, self.sub_agent.gamma_min))
print("epsilon: {0}, epsilon_decay: {1}, epsilon_min: {2}".format(
self.sub_agent.epsilon, self.sub_agent.epsilon_decay, self.sub_agent.epsilon_min))
def __init__(self, settings):
self.settings = settings
self.totalGameNo = settings['total_game_no']
self.playedGameNo = 0
self.simStepNo = settings['sim_step_no']
self.saveStepNo = settings['save_step_no']
self.display = settings['display']
self.env = ConnectFourEnv(self.display)
self.visited = {} # (stateStr, turn, action), visited
self.won = {} # (stateStr, turn, action), won
self.DRAW = -1
self.PLAYER = 1
self.OPP = 2
self.simpleAgent = SimpleAgent(self.env, self.OPP, self.PLAYER)
self.winnerResult = {self.DRAW:0, self.PLAYER:0, self.OPP:0}
self.greedyEpsilon = 0.1
self.startTime = time.strftime('%Y%m%d_%H%M%S')
logFile="output/%s.log" % (self.startTime)
util.Logger(logFile)
self.testMode = False
self.debugger = DebugInput(self).start()
def make_env(n_substeps=5, horizon=250, deterministic_mode=False):
'''
This make_env function is not used anywhere; it exists to provide a simple, bare-bones
example of how to construct a multi-agent environment using the modules framework.
'''
env = Base(n_agents=1,
n_substeps=n_substeps,
horizon=horizon,
floor_size=10,
grid_size=50,
deterministic_mode=deterministic_mode,
env_no=0,
action_lims=(-250.0, 250.0))
# Add Walls
#env.add_module(RandomWalls(grid_size=5, num_rooms=2, min_room_size=5, door_size=5, low_outside_walls=True, outside_wall_rgba="1 1 1 0.1"))
# Add Agents
first_agent_placement = custom_placement
agent_placement_fn = [first_agent_placement]
env.add_module(SimpleAgent(1, placement_fn=agent_placement_fn))
env.reset()
keys_self = ['agent_qpos_qvel']
keys_mask_self = [] #['mask_aa_obs']
keys_external = [] #['agent_qpos_qvel']
keys_mask_external = []
keys_copy = []
env = AddConstantObservationsWrapper(
env, new_obs={'target_pos': np.full((1, 1), 0.0)})
keys_self += ['target_pos']
env = SimpleWrapper(env)
env = SplitMultiAgentActions(env)
#env = DiscretizeActionWrapper(env, 'action_movement', nbuckets=21)
env = SplitObservations(env,
keys_self + keys_mask_self,
keys_copy=keys_copy)
env = DiscardMujocoExceptionEpisodes(env)
env = SelectKeysWrapper(env,
keys_self=keys_self,
keys_external=keys_external,
keys_mask=keys_mask_self + keys_mask_external,
flatten=False)
return env
def train_model(max_episodes=100):
"""
Trains a DQN agent to play the CartPole game by trial and error
:return: None
"""
# buffer = ReplayBuffer()
# Make environment
env = WhaleEnv(
config={
'active_player': 0,
'seed': datetime.utcnow().microsecond,
'env_num': 1,
'num_players': 5
})
# Set up agents
action_num = 3
agent = SimpleAgent(action_num=action_num, player_num=5)
agent_0 = NoDrawAgent(action_num=action_num)
agent_1 = NoDrawAgent(action_num=action_num)
agent_2 = NoDrawAgent(action_num=action_num)
agent_3 = NoDrawAgent(action_num=action_num)
# agent_train = RandomAgent(action_num=action_num)
agents = [agent, agent_0, agent_1, agent_2, agent_3]
# train_agents = [agent_train, agent_0, agent_1, agent_2, agent_3]
env.set_agents(agents)
agent.load_pretrained()
min_perf, max_perf = 1.0, 0.0
for episode_cnt in range(1, max_episodes + 1):
# print(f'{datetime.utcnow()} train ...')
loss = agent.train(
collect_gameplay_experiences(env, agents, GAME_COUNT_PER_EPISODE))
# print(f'{datetime.utcnow()} eval ...')
avg_rewards = evaluate_training_result(env, agents,
EVAL_EPISODES_COUNT)
# print(f'{datetime.utcnow()} calc ...')
if avg_rewards[0] > max_perf:
max_perf = avg_rewards[0]
agent.save_weight()
if avg_rewards[0] < min_perf:
min_perf = avg_rewards[0]
print('{0:03d}/{1} perf:{2:.2f}(min:{3:.2f} max:{4:.2f})'
'loss:{5:.4f} rewards:{6:.2f} {7:.2f} {8:.2f} {9:.2f}'.format(
episode_cnt, max_episodes, avg_rewards[0], min_perf,
max_perf, loss[0], avg_rewards[1], avg_rewards[2],
avg_rewards[3], avg_rewards[4]))
# env.close()
print('training end')
def main(_):
agent = SimpleAgent()
try:
while True:
with sc2_env.SC2Env(
map_name="Simple64",
players=[
sc2_env.Agent(sc2_env.Race.zerg),
sc2_env.Bot(sc2_env.Race.random,
sc2_env.Difficulty.very_easy)
],
agent_interface_format=features.AgentInterfaceFormat(
action_space=actions.ActionSpace.RAW,
use_raw_units=True,
raw_resolution=64,
),
) as env:
run_loop.run_loop([agent], env)
except KeyboardInterrupt:
pass
def test4():
env = ConnectFourEnv(display=True)
simpleAgent1 = SimpleAgent(env, 1, 2)
simpleAgent2 = SimpleAgent(env, 2, 1)
state = env.getState()
while True:
acttion1 = simpleAgent1.getAction(state)
state, gameOver, winner = env.act(1, acttion1, True)
time.sleep(0.3)
if gameOver:
break
acttion2 = simpleAgent2.getAction(state)
state, gameOver, winner = env.act(2, acttion2, True)
time.sleep(0.3)
if gameOver:
break
if winner == -1:
print 'Game draw'
else:
print 'Player %s won' % winner
time.sleep(5)
U_vi = value_iteration(epsilon=0.001)
"""
Collects and writes the results to a file for the Random Agent and
draws the graph
Draws:
Mean Reward per Episode vs Episode Number
"""
random_agent = RandomAgent(env_random)
process_data_random(env_random, random_agent, MAX_EPISODES,
MAX_ITERS_PER_EPISODE, REWARD_HOLE_SIMPLE, PROBLEM_ID)
"""
Collects and writes the results for the Simple Agent containing
data such as the number of iterations to reach the goal
"""
simple_agent = SimpleAgent(env_simple)
process_data_simple(env_simple, simple_agent, PROBLEM_ID)
"""
Collects and writes the results to a file for the Q-learning Agent
and draws the graphs.
Draws:
Mean Reward per Episode vs Episode Number
Utility Values in each State against Episode Number
"""
states = [i for i in range(64)]
q_learning_agent = QLearningAgent(env_qlearn, NE, RPLUS, GAMMA, ALPHA)
U = process_data_q(env_qlearn, q_learning_agent, MAX_EPISODES,
MAX_ITERS_PER_EPISODE, states, PROBLEM_ID, REWARD_HOLE_Q)
compare_utils(U_vi, U, 'Value itr', 'Q learning')
board = self.drop_piece(board, col, piece)
if self.check_if_winning(board, piece):
winner = piece
break
piece = piece % 2 + 1
self.agent1.game_over(winner)
self.agent2.game_over(winner)
return winner
def end(self):
self.agent1.teardown()
self.agent2.teardown()
# run agents
config = Config(6, 7, 4)
agents = [(RandomAgent(config), "rnd", 5000),
(SimpleAgent(config), "simple", 5000),
(OneStepLookaheadAgent(config), "1sla", 5000),
(OneStepLookaheadAgent(config), "1sla", 5000),
(OneStepLookaheadAgent(config), "1sla", 5000),
(NStepsLookaheadAgent(config, 2), "2sla", 3000),
(NStepsLookaheadAgent(config, 3), "3sla", 5000)]
for agent, agent_name, nruns in agents:
training = Training(config, agent, CNNAgent(config, Network1(),
agent_name))
for n in range(nruns):
winner = training.run()
print("Agent", agent_name, ", game", n, "- player", winner, "wins")
training.end()
def run_training(
opponent,
mcts_opp,
game_state_file,
graph_file,
model_save_file,
mcts_iters,
temp,
tempsteps,
lr,
discount,
memsize,
num_episodes,
num_epochs,
batch_size,
train_every,
save_every,
graph_every,
averaging_window,
opt_eps=1e-8,
ucb_c=1.5,
boardsize=8,
inputs=20,
render=False,
verbose=False,
):
env = PommermanEnvironment(
render=render,
num_agents=2,
game_state_file=game_state_file,
)
run_settings = RunSettings(
num_episodes=num_episodes,
num_epochs=num_epochs,
batch_size=batch_size,
train_every=train_every,
save_every=save_every,
graph_every=graph_every,
averaging_window=averaging_window,
graph_file=graph_file,
verbose=verbose,
)
agent_settings = AgentSettings(
optimizer=torch.optim.Adam,
learning_rate=lr,
opt_eps=opt_eps,
epsilon_max=0,
epsilon_min=0,
epsilon_duration=0,
verbose=verbose,
)
memory = MCTSMemory(buffer_len=memsize, discount=discount)
if mcts_opp is None:
mcts_opp = opponent
if mcts_opp == 'rand':
opp = pommerman.agents.RandomAgent()
elif mcts_opp == 'noop':
opp = PommermanNoopAgent()
elif mcts_opp == 'simp':
opp = pommerman.agents.SimpleAgent()
else:
raise Exception('Invalid MCTS opponent type', mcts_opp)
mcts_model = ActorCriticNet(board_size=boardsize, in_channels=inputs)
agent1 = MCTSAgent(
mcts_iters=mcts_iters,
discount=discount,
c=ucb_c,
temp=temp,
tempsteps=tempsteps,
agent_id=0,
opponent=opp,
model_save_file=model_save_file,
model=mcts_model,
settings=agent_settings,
memory=memory,
)
agent1.load()
if opponent == 'rand':
agent2 = RandomAgent()
elif opponent == 'noop':
agent2 = NoopAgent()
elif opponent == 'simp':
agent2 = SimpleAgent()
else:
raise Exception('Invalid opponent type', opponent)
experiment = Experiment([agent1, agent2], env, run_settings)
experiment.train()
import sys
import platform
from absl import logging
from absl import app
from absl import flags
from pysc2.env import sc2_env
from pysc2.env import run_loop
from pysc2.env import remote_sc2_env
# !!! LOAD YOUR BOT HERE !!!
from simple_agent import SimpleAgent
AGENT = SimpleAgent()
RACE = sc2_env.Race.protoss
STEP_MUL = 8
AGENT_INTERFACE_FORMAT = sc2_env.parse_agent_interface_format(
feature_screen=84,
feature_minimap=64,
rgb_screen=None,
rgb_minimap=None,
action_space="FEATURES", #FEATURES or RGB
use_feature_units=False)
# Flags
FLAGS = flags.FLAGS
flags.DEFINE_integer("GamePort", None, "GamePort")
flags.DEFINE_integer("StartPort", None, "StartPort")
flags.DEFINE_string("LadderServer", "127.0.0.1", "LadderServer")
flags.DEFINE_string("OpponentId", None, "OpponentId")
print("Unrecognized ship name {}".format(args.ship2))
print("Recognized ship names are:\n")
for name in ship_templates.keys():
print("\t{}".format(name))
exit(1)
for distance in args.ranges:
if distance not in ["long", "medium", "short"]:
print("Unknown range for ship combat: {}".format(distance))
sys.exit(1)
# Set up logging to track what happens during the die rolling.
logging.basicConfig(filename='joust.log', level=logging.DEBUG)
# Agent for the simulation
agent = SimpleAgent()
# Loop through all pairs and have them joust
for ship_name_1 in first_ship_names:
ship_1 = ship.Ship(name=ship_name_1,
template=ship_templates[ship_name_1],
upgrades=[],
player_number=1)
for ship_name_2 in second_ship_names:
for attack_range in ["long", "medium", "short"]:
# Make sure we are actually rolling dice
a_colors, a_roll = ship_1.roll("front", attack_range)
if 0 < len(a_colors):
roll_counts = []
print("{} vs {} at range {}".format(ship_name_1, ship_name_2,
attack_range))
class DecompAgent:
"""
This agent takes advantage of the problem sub-structure by decomposing the
root problem into a navigation subproblem (which it solves using a simple
Q-learning agent) and using hand-crafted heuristics for all other decisions.
"""
def __init__(self):
# Only four navigation actions for subproblem actions
nA = 4
# Only taxi row, column and destination index for subproblem states
states_shape = (5, 5, 4)
self.sub_agent = SimpleAgent(states_shape=states_shape, nA=nA)
# Learning rate / step size
self.sub_agent.alpha = 0.01
self.sub_agent.alpha_decay = 1
self.sub_agent.alpha_min = 0
# Discount
self.sub_agent.gamma = 1
self.sub_agent.gamma_decay = 1
self.sub_agent.gamma_min = 0
# Exploration
self.sub_agent.epsilon = 0.01
self.sub_agent.epsilon_decay = 1
self.sub_agent.epsilon_min = 0
# For our params, just mimic the sub-agent's
(self.alpha, self.epsilon, self.gamma) = \
self.sub_agent.alpha, self.sub_agent.epsilon, self.sub_agent.gamma
# Environment priors
self.action_pickup = 4
self.action_dropoff = 5
self.locs = [(0, 0), (0, 4), (4, 0), (4, 3)]
self.passenger_in_taxi_idx = 4
print("alpha: {0}, alpha_decay: {1}, alpha_min: {2}".format(
self.sub_agent.alpha, self.sub_agent.alpha_decay, self.sub_agent.alpha_min))
print("gamma: {0}, gamma_decay: {1}, gamma_min: {2}".format(
self.sub_agent.gamma, self.sub_agent.gamma_decay, self.sub_agent.gamma_min))
print("epsilon: {0}, epsilon_decay: {1}, epsilon_min: {2}".format(
self.sub_agent.epsilon, self.sub_agent.epsilon_decay, self.sub_agent.epsilon_min))
def select_action(self, state):
# Override epsilon-greedy exploration for pickup/dropoff
if self.can_pick_up(state):
return self.action_pickup
if self.can_drop_off(state):
return self.action_dropoff
# Otherwise, defer to the sub-agent
transformed_state = self.transform_state(state)
return self.sub_agent.select_action(transformed_state)
def step(self, state, action, reward, next_state, done):
# Transform experience into the problem space of the sub-agent
# If the selected action was pickup/dropoff, then experience is not
# relevant to sub-problem
if action == self.action_pickup or action == self.action_dropoff:
return
# If we can pickup/dropoff in the next state, then for the
# sub-problem we consider next_state to be terminal and the episode
# concluded
if self.can_pick_up(next_state) or self.can_drop_off(next_state):
state_t = self.transform_state(state)
action_t = self.transform_action(action)
reward_t = 9 # end of episode reward
next_state_t = self.transform_state(next_state)
done_t = True
# Otherwise, transform relatively unchanged for sub-problem
else:
state_t = self.transform_state(state)
action_t = self.transform_action(action)
reward_t = -1
next_state_t = self.transform_state(next_state)
done_t = False
# Pass transformed experience to sub-agent
self.sub_agent.step(state_t, action_t, reward_t, next_state_t, done_t)
(self.alpha, self.epsilon, self.gamma) = \
self.sub_agent.alpha, self.sub_agent.epsilon, self.sub_agent.gamma
def transform_state(self, state):
"""Transform state into the problem space of the sub-agent"""
taxi_row, taxi_col, pass_idx, dest_idx = self.decode_state(state)
# If we don't have the passenger, passenger is our destination
if pass_idx != self.passenger_in_taxi_idx:
dest_idx_t = pass_idx
# If we have the passenger, destination is our destination
else:
dest_idx_t = dest_idx
# Encode in subproblem state space and return
return (taxi_row, taxi_col, dest_idx_t)
def transform_action(self, action):
# Action space is the same, minus the final two actions
assert action != self.action_pickup
assert action != self.action_dropoff
return action
def can_pick_up(self, state):
taxi_row, taxi_col, pass_idx, dest_idx = self.decode_state(state)
# Can't pickup if passenger already in taxi
if pass_idx == self.passenger_in_taxi_idx:
return False
# Otherwise, taxi must be colocated with passenger
return (taxi_row, taxi_col) == self.locs[pass_idx]
def can_drop_off(self, state):
taxi_row, taxi_col, pass_idx, dest_idx = self.decode_state(state)
# Can't dropoff if passenger not in taxi
if pass_idx != self.passenger_in_taxi_idx:
return False
# Otherwise, taxi must be colocated with destination
return (taxi_row, taxi_col) == self.locs[dest_idx]
def decode_state(self, i):
out = []
out.append(i % 4)
i = i // 4
out.append(i % 5)
i = i // 5
out.append(i % 5)
i = i // 5
out.append(i)
assert 0 <= i < 5
taxi_row, taxi_col, pass_idx, dest_idx = reversed(out)
return taxi_row, taxi_col, pass_idx, dest_idx
class MCTS:
def __init__(self, settings):
self.settings = settings
self.totalGameNo = settings['total_game_no']
self.playedGameNo = 0
self.simStepNo = settings['sim_step_no']
self.saveStepNo = settings['save_step_no']
self.display = settings['display']
self.env = ConnectFourEnv(self.display)
self.visited = {} # (stateStr, turn, action), visited
self.won = {} # (stateStr, turn, action), won
self.DRAW = -1
self.PLAYER = 1
self.OPP = 2
self.simpleAgent = SimpleAgent(self.env, self.OPP, self.PLAYER)
self.winnerResult = {self.DRAW:0, self.PLAYER:0, self.OPP:0}
self.greedyEpsilon = 0.1
self.startTime = time.strftime('%Y%m%d_%H%M%S')
logFile="output/%s.log" % (self.startTime)
util.Logger(logFile)
self.testMode = False
self.debugger = DebugInput(self).start()
def initializeProcesses(self):
# Multi process jobs
self.multiCpuNo = self.settings['multi_cpu_no']
self.queueList = []
self.processList = []
self.queueChild2Parent = Queue()
for i in range(self.multiCpuNo):
queueParent2Child = Queue()
self.queueList.append(queueParent2Child)
#print 'creating a child process[%s]' % i
p = Process(target=self.simulateOne, args=(i, self.simStepNo / self.multiCpuNo,
queueParent2Child, self.queueChild2Parent))
p.start()
self.processList.append(p)
def __getstate__(self):
d = dict(self.__dict__)
del d['queueList']
del d['processList']
del d['queueChild2Parent']
return d
def printEnv(self):
print 'Start time: %s' % self.startTime
print '[ Running Environment ]'
for key in self.settings.keys():
print '{} : '.format(key).ljust(30) + '{}'.format(self.settings[key])
print 'width: %s, height: %s' % (self.env.width, self.env.height)
def getStateStr(self, state):
#return np.array_str(state)
return hash(state.tostring())
def simulate(self, orgState):
time1 = time.time()
for i in range(self.multiCpuNo):
self.queueList[i].put((orgState, self.visited, self.won))
finishedChildNo = 0
for i in range(self.multiCpuNo):
childID, winnerList, historyList, expandedList = self.queueChild2Parent.get()
for expandedNode in expandedList:
if expandedNode not in self.visited:
self.visited[expandedNode] = 0
self.won[expandedNode] = 0
for winner, history in zip(winnerList, historyList):
self.updateTreeInfo(winner, history)
finishedChildNo += 1
#print 'simulateOne done %s' % childID
if finishedChildNo == self.multiCpuNo:
break
#print 'all simulateOne finished'
time2 = time.time()
#print 'simulte took %.2f sec' % (time2 - time1)
def simulateOne(self, id, simStepNo, queueParent2Child, queueChild2Parent):
while True:
orgState, visited, won = queueParent2Child.get()
self.visited = visited
self.won = won
self.env.reset()
self.env.setState(orgState)
self.visited['haha'] = 'dj'
historyList = []
winnerList = []
expandedList = []
state = orgState.copy()
turn = self.PLAYER
history = []
expanded = False
for i in range(simStepNo):
if turn == self.PLAYER:
availableActions = self.env.availableActions(state)
stateStr = self.getStateStr(state)
totalStateVisited = 0
# check every actions are visited before
for action in availableActions:
stateActionPair = (stateStr, turn, action)
if stateActionPair in self.visited:
totalStateVisited += self.visited[stateActionPair]
else:
totalStateVisited = 0
if totalStateVisited == 0:
action = self.getRandomAction(state)
else:
maxUpperBound = 0
for action in availableActions:
stateActionPair = (stateStr, turn, action)
won = self.won.get(stateActionPair, 0)
visited = max(self.visited.get(stateActionPair, 1), 1)
winRatio = float(won) / visited
upperBound = winRatio + math.sqrt(2 * math.log(totalStateVisited) / visited)
if upperBound >= maxUpperBound:
maxUpperBound = upperBound
selectedAction = action
action = selectedAction
elif turn == self.OPP:
if 'sim_opp_policy' in self.settings and self.settings['sim_opp_policy'] == 'simple':
action = self.simpleAgent.getAction(state)
else:
action = self.getRandomAction(state)
stateStr = self.getStateStr(state)
stateActionPair = (stateStr, turn, action)
if expanded == False and stateActionPair not in self.visited:
canExpand = True
expanded = True
else:
canExpand = False
state, gameOver, winner = self.doAction(state, action, turn, history, expandedList, canExpand, False)
if turn == self.PLAYER:
turn = self.OPP
else:
turn = self.PLAYER
if gameOver:
self.updateTreeInfo(winner, history)
historyList.append(history)
winnerList.append(winner)
# restart sim
self.env.reset()
self.env.setState(orgState)
state = orgState.copy()
turn = self.PLAYER
history = []
expanded = False
continue
queueChild2Parent.put((id, winnerList, historyList, expandedList))
def getRandomAction(self, state, availableActions=None):
if availableActions == None:
availableActions = self.env.availableActions(state)
actionIndex = random.randint(0, len(availableActions)-1)
return availableActions[actionIndex]
def getAction(self, state, turn):
availableActions = self.env.availableActions(state)
if len(availableActions) == 1:
return availableActions[0]
maxAction = -1
maxWinRatio = 0
availableActions = self.env.availableActions(state)
stateStr = self.getStateStr(state)
for action in availableActions:
stateActionPair = (stateStr, turn, action)
if stateActionPair not in self.visited:
continue
winRatio = float(self.won.get(stateActionPair, 0)) / max(self.visited.get(stateActionPair, 1), 1)
if winRatio >= maxWinRatio:
maxWinRatio = winRatio
maxAction = action
return maxAction
def doAction(self, state, action, turn, history, expandedList, canExpand, display):
newState, gameOver, winner = self.env.act(turn, action, display)
stateStr = self.getStateStr(state)
stateActionPair = (stateStr, turn, action)
if stateActionPair not in self.visited and canExpand:
self.visited[stateActionPair] = 0
self.won[stateActionPair] = 0
if expandedList != None:
expandedList.append(stateActionPair)
history.append(stateActionPair)
return newState, gameOver, winner
def updateTreeInfo(self, winner, history):
""" Update win result from the current node to the top node """
for stateActionPair in history:
if stateActionPair in self.visited:
self.visited[stateActionPair] += 1
_, turn, _ = stateActionPair
if turn == winner:
self.won[stateActionPair] += 1
def printHistory(self, history):
step = 0
print '\n[ history ]'
for stateActionPair in history:
state, turn, action = stateActionPair
if stateActionPair in self.visited:
visited = self.visited[stateActionPair]
won = self.won[stateActionPair]
else:
visited = 0
won = 0
print 'step[%s] turn=%s, action=%s, visited=%s, won=%s' % \
(step, turn, action, visited, won)
step += 1
print ''
def printResult(self):
print 'total states: %s' % len(self.visited)
def save(self, step):
if os.path.exists('snapshot') == False:
os.makedirs('snapshot')
fileName = 'snapshot/mcts_%s' % step
with open(fileName + '.pickle', 'wb') as f:
pickle.dump(self, f)
def gogo(self):
self.initializeProcesses()
lastResult = []
lastResultWin = 0
for i in range(self.totalGameNo):
self.env.reset()
state = self.env.getState()
history = []
turn = random.randint(self.PLAYER, self.OPP)
startTime = time.time()
while True:
if turn == self.PLAYER:
self.simulate(state)
if settings['player_action'] == 'egreedy':
action = self.getActionEGreedy(state, self.PLAYER)
else:
action = self.getAction(state, self.PLAYER)
elif turn == self.OPP:
if settings['opponent'] == 'user':
action = self.env.getManualAction(state)
else:
action = self.simpleAgent.getAction(state)
state, gameOver, winner = self.doAction(state, action, turn, history, None, True, True)
if gameOver:
break
if turn == self.PLAYER:
turn = self.OPP
else:
turn = self.PLAYER
elapsed = time.time() - startTime
if settings['opponent'] == 'user':
self.env.showWinner(winner)
self.playedGameNo += 1
self.winnerResult[winner] += 1
if winner == -1:
print 'Game draw'
else:
mcts.updateTreeInfo(winner, history)
if winner == self.PLAYER:
lastResultWin += 1
if len(lastResult) == 100:
todel = lastResult.pop(0)
if todel == 1:
lastResultWin -= 1
lastResult.append(winner)
lastRatio = float(lastResultWin) * 100 / len(lastResult)
#mcts.printResult()
winRatio = float(self.winnerResult[self.PLAYER]) * 100 \
/ (self.winnerResult[self.OPP] + self.winnerResult[self.PLAYER])
if winner == 1:
winStr = 'Win'
else:
winStr = 'Lose'
print 'Game %s : %s, %s, total=%.0f%%, last 100=%.0f%%, %.1fs' % (self.playedGameNo, self.winnerResult, winStr, winRatio, lastRatio, elapsed)
if self.playedGameNo % self.saveStepNo == 0:
self.save(self.playedGameNo)
#time.sleep(5)
self.debugger.finish()
