def main():
env = Environment(config2)
state_size = (config2.vision_range * 2 +
1)**2 * config2.color_num_dims + config2.scent_num_dims
agent = RLAgent(env, state_size=state_size)
agent._load("outputs/models/NELQ_190000")
# ._load("NELQ.model")
# optimizer = optim.Adam(agent.policy.parameters())
# print list(agent.policy.parameters())
test(agent, env)
def main(args, prt):
config = tf.ConfigProto() #tensorflow的参数配置对象
config.gpu_options.allow_growth = True #动态申请显存，需要多少就申请多少
sess = tf.Session(config=config)
# 加载任务特定的类
DataGenerator, Env, reward_func, AttentionActor, AttentionCritic = \
load_task_specific_components(args['task_name'])
dataGen = DataGenerator(args)
dataGen.reset()
env = Env(args)
#创建一个 RL（强化学习）代理
agent = RLAgent(args,
prt,
env,
dataGen,
reward_func,
AttentionActor,
AttentionCritic,
is_train=args['is_train'])
agent.Initialize(sess)
#训练或评估
start_time = time.time() # 计算时间用的，记录开始时间
if args['is_train']:# 如果参数是训练
prt.print_out('训练开始')
train_time_beg = time.time() # 开始训练的时间
for step in range(args['n_train']):
summary = agent.run_train_step()
_, _ , actor_loss_val, critic_loss_val, actor_gra_and_var_val, critic_gra_and_var_val,\
R_val, v_val, logprobs_val,probs_val, actions_val, idxs_val= summary
if step%args['save_interval'] == 0:
agent.saver.save(sess,args['model_dir']+'/model.ckpt', global_step=step)#sess是之前tensorflow的初始化对象
if step%args['log_interval'] == 0:
train_time_end = time.time()-train_time_beg
prt.print_out('训练步数: {} -- 时间: {} -- 训练奖励: {} -- 值: {}'\
.format(step,time.strftime("%H:%M:%S", time.gmtime(\
train_time_end)),np.mean(R_val),np.mean(v_val)))
prt.print_out(' actor loss: {} -- critic loss: {}'\
.format(np.mean(actor_loss_val),np.mean(critic_loss_val)))
train_time_beg = time.time()
if step%args['test_interval'] == 0:
agent.inference(args['infer_type'])
else: # 否则就是在推论
prt.print_out('评估开始')
agent.inference(args['infer_type'])
prt.print_out('总时间为： {}'.format(\
time.strftime("%H:%M:%S", time.gmtime(time.time()-start_time)))) # 运行完毕之后输出运行总时间
def getAgentsAndAdjudicator():
agents = [None, None]
idx = np.random.randint(2)
agents[idx] = RLAgent(idx + 1, None)
agents[1 - idx], adjudicator = getOpponentRandomly(2 - idx)
# print("RL Agent: (%d, %d) Opponent: (%d, %d)" %(idx, idx+1, 1-idx, 2-idx))
return agents, adjudicator, idx + 1
def main():
env = Environment(config2)
from agent import actions
state_size = (config2.vision_range*2 + 1)**2 * config2.color_num_dims + config2.scent_num_dims + len(actions)
agent = RLAgent(env, state_size=state_size)
optimizer = optim.Adam(agent.policy.parameters(),
lr=agent_config['learning_rate'])
setup_output_dir()
train(agent, env, [0, 1, 2, 3], optimizer)
def delete_temp(self, old_model, temp_model):
"""
Delete temporary files in case of 2 versions of same model
(but different times of training). Keep the best model and
delete the rest.
Parameters
----------
old_model: string
Model filename before training.
temp_model: string
Temporary model filename (after training).
Return
------
use_training: boolean
False only if old model wins against new temp model.
True otherwise.
"""
use_training = True
# Several versions of same model
if (old_model is not None) and (temp_model == old_model + '_temp'):
# Confront them
agent1 = RLAgent()
agent1.load_model(old_model)
agent2 = RLAgent()
agent2.load_model(temp_model)
results = compare_agents(agent1,
agent2,
n_games=10,
time_limit=100,
verbose=False)
# Keep best
if results[3] >= results[2]:
# More trained agent is the best
os.remove('Models/' + old_model + '.csv')
os.remove('Models/data/count_' + old_model + '.csv')
os.rename(r'Models/' + temp_model + '.csv',
r'Models/' + old_model + '.csv')
os.rename(r'Models/data/count_' + temp_model + '.csv',
r'Models/data/count_' + old_model + '.csv')
else:
# Less trained agent is the best
os.remove('Models/' + temp_model + '.csv')
os.remove('Models/data/count_' + temp_model + '.csv')
use_training = False
return use_training
import torch
import random
from game import Game
from agent import RLAgent
from stupid_ai import StupidAI
game = Game()
agent = RLAgent()
stupid_ai = StupidAI()
agent.state_values = torch.load('./model.pth')
def check_board():
win_or_tie = True
if game.who_wins() == -1: # Human win
print('==================> You win!')
game.clear()
elif game.who_wins() == 1: # AI win
print('==================> You lose..')
game.clear()
elif game.who_wins() == 2: # Tie
print('==================> Tie!')
game.clear()
else:
win_or_tie = False
return win_or_tie
while True:
x, y = stupid_ai.human_move()
if game.board[y, x] != 0:
def tournament(self, change_opp=False):
"""
Method to rank the different models obtained after any training.
For the values of the factor epsilon of an RL Agent, declared
in init method, this method creates a tournament for the
corresponding different models. Each model plays 10 games
against all others and the scores of each model against another
are stored in a CSV file. A TXT file is also generated using
the CSV file: it displays rankings of each model, alongside
its total score against all other models.
Parameter
---------
change_opp: boolean
Set to True to consider agents trained with mixed opponents
participating to the tournament.
Outputs
-------
Tournament report: CSV file
Located at: 'Models/results/(self.tournament_name).csv'.
File storing the results of each confrontation between 2
agents.
Tournament ranking: TXT file
Located at: 'Models/results/(self.tournament_name).txt'.
File ranking the agents using the results of the tournament,
with total score of each agent displayed.
"""
n_players = len(self.epsilon_values) * (
(int(self.random_training) + int(self.self_training)) *
(1 + int(change_opp)))
print('-----------------------------')
print('TOURNAMENT with {} agents'.format(n_players))
print('-----------------------------\n')
# Initialization of scores: some rows and columns are
# only used for saving configurations of models
# (epsilon, opponents, change of opponent).
scores = -np.ones((n_players + 3, n_players + 3))
# List of opponent kinds
training_ways = []
if self.random_training:
training_ways.append('Random')
if change_opp:
training_ways.append('RandomvsSelf')
if self.self_training:
training_ways.append('Self')
if change_opp:
training_ways.append('SelfvsRandom')
# List of players
players = [[epsilon, training_way] for epsilon in self.epsilon_values
for training_way in training_ways]
for idx1, player1 in enumerate(players):
epsilon1 = player1[0]
training_way1 = player1[1]
filename = ('greedy' + str(epsilon1)[0] + '_' + str(epsilon1)[2:] +
'_vs' + training_way1)
# Load first agent
agent1 = RLAgent()
agent1.load_model(filename)
# Save config of agent1
scores[idx1 + 3, 0] = epsilon1
# 0: RANDOM | 1: SELF
scores[idx1 + 3, 1] = (int(training_way1 == 'Self') +
int(training_way1 == 'SelfvsRandom'))
# -1: nothing | 0: Random vs Self | 1: Self vs Random
scores[idx1 + 3,
2] = -1 + (2 * int(training_way1 == 'SelfvsRandom') +
int(training_way1 == 'RandomvsSelf'))
for idx2, player2 in enumerate(players):
epsilon2 = player2[0]
training_way2 = player2[1]
filename = ('greedy' + str(epsilon2)[0] + '_' +
str(epsilon2)[2:] + '_vs' + training_way2)
# Load second agent
agent2 = RLAgent()
agent2.load_model(filename)
# Save config of agent2
scores[0, idx2 + 3] = epsilon2
scores[1, idx2 + 3] = (int(training_way2 == 'Self') +
int(training_way2 == 'SelfvsRandom'))
scores[2, idx2 +
3] = -1 + (2 * int(training_way2 == 'SelfvsRandom') +
int(training_way2 == 'RandomvsSelf'))
print('Current match:')
print('Player1: epsilon = {}, trained vs {}'.format(
epsilon1, training_way1))
print('Player2: epsilon = {}, trained vs {}'.format(
epsilon2, training_way2))
results = compare_agents(agent1,
agent2,
n_games=10,
time_limit=100,
verbose=False)
# Score of agent1
scores[idx1 + 3, idx2 + 3] = results[2]
# Score of agent2
scores[idx2 + 3, idx1 + 3] = results[3]
print('------')
# Update tournament file name
name = self.tournament_name[:-1]
nbr = int(self.tournament_name[-1])
nbr += 1
self.tournament_name = name + str(nbr)
# Save tournament
np.savetxt(str('Models/results/' + self.tournament_name + '.csv'),
scores,
delimiter=',')
# Rank players
self.tournament_ranking(self.tournament_name, self.tournament_name)
print('Results of tournament are stored in {}.csv and {}.txt\n'.format(
self.tournament_name, self.tournament_name))
rl_agent.win()
else:
# print("Draw!")
rl_agent.lose()
break
board.print()
action = int(input())-1
board.update_state(player="O", action=action)
done = board.check()
if done != 2:
# if done == -1:
# print("Game Over!")
# elif done == 0:
# print("Draw!")
rl_agent.lose()
break
board.print()
board.print()
print("V: ", rl_agent.wins, ", L: ", rl_agent.loses," D: ", experiments-rl_agent.loses-rl_agent.wins)
board = Board(print_map=False)
rl_agent = RLAgent(_id = "X")
opponent = Agent(_id = "O")
train(board, rl_agent, opponent)
rl_agent.another_opponent()
test(board, rl_agent)
def train(config):
# train env
print('Setting up TextWorld environment...')
batch_size = config['training']['scheduling']['batch_size']
env_id = gym_textworld.make_batch(env_id=config['general']['env_id'],
batch_size=batch_size,
parallel=True)
env = gym.make(env_id)
env.seed(config['general']['random_seed'])
# valid and test env
run_test = config['general']['run_test']
if run_test:
test_batch_size = config['training']['scheduling']['test_batch_size']
# valid
valid_env_name = config['general']['valid_env_id']
valid_env_id = gym_textworld.make_batch(env_id=valid_env_name,
batch_size=test_batch_size,
parallel=True)
valid_env = gym.make(valid_env_id)
valid_env.seed(config['general']['random_seed'])
# test
test_env_name_list = config['general']['test_env_id']
assert isinstance(test_env_name_list, list)
test_env_id_list = [
gym_textworld.make_batch(env_id=item,
batch_size=test_batch_size,
parallel=True)
for item in test_env_name_list
]
test_env_list = [
gym.make(test_env_id) for test_env_id in test_env_id_list
]
for i in range(len(test_env_list)):
test_env_list[i].seed(config['general']['random_seed'])
print('Done.')
# Set the random seed manually for reproducibility.
np.random.seed(config['general']['random_seed'])
torch.manual_seed(config['general']['random_seed'])
if torch.cuda.is_available():
if not config['general']['use_cuda']:
logger.warning(
"WARNING: CUDA device detected but 'use_cuda: false' found in config.yaml"
)
else:
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(config['general']['random_seed'])
else:
config['general']['use_cuda'] = False # Disable CUDA.
revisit_counting = config['general']['revisit_counting']
replay_batch_size = config['general']['replay_batch_size']
replay_memory_capacity = config['general']['replay_memory_capacity']
replay_memory_priority_fraction = config['general'][
'replay_memory_priority_fraction']
word_vocab = dict2list(env.observation_space.id2w)
word2id = {}
for i, w in enumerate(word_vocab):
word2id[w] = i
# collect all nouns
verb_list = ["go", "take"]
object_name_list = ["east", "west", "north", "south", "coin"]
verb_map = [word2id[w] for w in verb_list if w in word2id]
noun_map = [word2id[w] for w in object_name_list if w in word2id]
agent = RLAgent(
config,
word_vocab,
verb_map,
noun_map,
replay_memory_capacity=replay_memory_capacity,
replay_memory_priority_fraction=replay_memory_priority_fraction)
init_learning_rate = config['training']['optimizer']['learning_rate']
exp_dir = get_experiment_dir(config)
summary = SummaryWriter(exp_dir)
parameters = filter(lambda p: p.requires_grad, agent.model.parameters())
if config['training']['optimizer']['step_rule'] == 'sgd':
optimizer = torch.optim.SGD(parameters, lr=init_learning_rate)
elif config['training']['optimizer']['step_rule'] == 'adam':
optimizer = torch.optim.Adam(parameters, lr=init_learning_rate)
log_every = 100
reward_avg = SlidingAverage('reward avg', steps=log_every)
step_avg = SlidingAverage('step avg', steps=log_every)
loss_avg = SlidingAverage('loss avg', steps=log_every)
# save & reload checkpoint only in 0th agent
best_avg_reward = -10000
best_avg_step = 10000
# step penalty
discount_gamma = config['general']['discount_gamma']
provide_prev_action = config['general']['provide_prev_action']
# epsilon greedy
epsilon_anneal_epochs = config['general']['epsilon_anneal_epochs']
epsilon_anneal_from = config['general']['epsilon_anneal_from']
epsilon_anneal_to = config['general']['epsilon_anneal_to']
# counting reward
revisit_counting_lambda_anneal_epochs = config['general'][
'revisit_counting_lambda_anneal_epochs']
revisit_counting_lambda_anneal_from = config['general'][
'revisit_counting_lambda_anneal_from']
revisit_counting_lambda_anneal_to = config['general'][
'revisit_counting_lambda_anneal_to']
epsilon = epsilon_anneal_from
revisit_counting_lambda = revisit_counting_lambda_anneal_from
for epoch in range(config['training']['scheduling']['epoch']):
agent.model.train()
obs, infos = env.reset()
agent.reset(infos)
print_command_string, print_rewards = [[] for _ in infos
], [[] for _ in infos]
print_interm_rewards = [[] for _ in infos]
print_rc_rewards = [[] for _ in infos]
dones = [False] * batch_size
rewards = None
avg_loss_in_this_game = []
new_observation_strings = agent.get_observation_strings(infos)
if revisit_counting:
agent.reset_binarized_counter(batch_size)
revisit_counting_rewards = agent.get_binarized_count(
new_observation_strings)
current_game_step = 0
prev_actions = ["" for _ in range(batch_size)
] if provide_prev_action else None
input_description, description_id_list = agent.get_game_step_info(
obs, infos, prev_actions)
while not all(dones):
v_idx, n_idx, chosen_strings, state_representation = agent.generate_one_command(
input_description, epsilon=epsilon)
obs, rewards, dones, infos = env.step(chosen_strings)
new_observation_strings = agent.get_observation_strings(infos)
if provide_prev_action:
prev_actions = chosen_strings
# counting
if revisit_counting:
revisit_counting_rewards = agent.get_binarized_count(
new_observation_strings, update=True)
else:
revisit_counting_rewards = [0.0 for _ in range(batch_size)]
agent.revisit_counting_rewards.append(revisit_counting_rewards)
revisit_counting_rewards = [
float(format(item, ".3f")) for item in revisit_counting_rewards
]
for i in range(len(infos)):
print_command_string[i].append(chosen_strings[i])
print_rewards[i].append(rewards[i])
print_interm_rewards[i].append(infos[i]["intermediate_reward"])
print_rc_rewards[i].append(revisit_counting_rewards[i])
if type(dones) is bool:
dones = [dones] * batch_size
agent.rewards.append(rewards)
agent.dones.append(dones)
agent.intermediate_rewards.append(
[info["intermediate_reward"] for info in infos])
# computer rewards, and push into replay memory
rewards_np, rewards, mask_np, mask = agent.compute_reward(
revisit_counting_lambda=revisit_counting_lambda,
revisit_counting=revisit_counting)
curr_description_id_list = description_id_list
input_description, description_id_list = agent.get_game_step_info(
obs, infos, prev_actions)
for b in range(batch_size):
if mask_np[b] == 0:
continue
if replay_memory_priority_fraction == 0.0:
# vanilla replay memory
agent.replay_memory.push(curr_description_id_list[b],
v_idx[b], n_idx[b], rewards[b],
mask[b], dones[b],
description_id_list[b],
new_observation_strings[b])
else:
# prioritized replay memory
is_prior = rewards_np[b] > 0.0
agent.replay_memory.push(is_prior,
curr_description_id_list[b],
v_idx[b], n_idx[b], rewards[b],
mask[b], dones[b],
description_id_list[b],
new_observation_strings[b])
if current_game_step > 0 and current_game_step % config["general"][
"update_per_k_game_steps"] == 0:
policy_loss = agent.update(replay_batch_size,
discount_gamma=discount_gamma)
if policy_loss is None:
continue
loss = policy_loss
# Backpropagate
optimizer.zero_grad()
loss.backward(retain_graph=True)
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm(
agent.model.parameters(),
config['training']['optimizer']['clip_grad_norm'])
optimizer.step() # apply gradients
avg_loss_in_this_game.append(to_np(policy_loss))
current_game_step += 1
agent.finish()
avg_loss_in_this_game = np.mean(avg_loss_in_this_game)
reward_avg.add(agent.final_rewards.mean())
step_avg.add(agent.step_used_before_done.mean())
loss_avg.add(avg_loss_in_this_game)
# annealing
if epoch < epsilon_anneal_epochs:
epsilon -= (epsilon_anneal_from -
epsilon_anneal_to) / float(epsilon_anneal_epochs)
if epoch < revisit_counting_lambda_anneal_epochs:
revisit_counting_lambda -= (
revisit_counting_lambda_anneal_from -
revisit_counting_lambda_anneal_to
) / float(revisit_counting_lambda_anneal_epochs)
# Tensorboard logging #
# (1) Log some numbers
if (epoch + 1
) % config["training"]["scheduling"]["logging_frequency"] == 0:
summary.add_scalar('avg_reward', reward_avg.value, epoch + 1)
summary.add_scalar('curr_reward', agent.final_rewards.mean(),
epoch + 1)
summary.add_scalar('curr_interm_reward',
agent.final_intermediate_rewards.mean(),
epoch + 1)
summary.add_scalar('curr_counting_reward',
agent.final_counting_rewards.mean(), epoch + 1)
summary.add_scalar('avg_step', step_avg.value, epoch + 1)
summary.add_scalar('curr_step', agent.step_used_before_done.mean(),
epoch + 1)
summary.add_scalar('loss_avg', loss_avg.value, epoch + 1)
summary.add_scalar('curr_loss', avg_loss_in_this_game, epoch + 1)
msg = 'E#{:03d}, R={:.3f}/{:.3f}/IR{:.3f}/CR{:.3f}, S={:.3f}/{:.3f}, L={:.3f}/{:.3f}, epsilon={:.4f}, lambda_counting={:.4f}'
msg = msg.format(epoch, np.mean(reward_avg.value),
agent.final_rewards.mean(),
agent.final_intermediate_rewards.mean(),
agent.final_counting_rewards.mean(),
np.mean(step_avg.value),
agent.step_used_before_done.mean(),
np.mean(loss_avg.value), avg_loss_in_this_game,
epsilon, revisit_counting_lambda)
if (epoch + 1
) % config["training"]["scheduling"]["logging_frequency"] == 0:
print("=========================================================")
for prt_cmd, prt_rew, prt_int_rew, prt_rc_rew in zip(
print_command_string, print_rewards, print_interm_rewards,
print_rc_rewards):
print("------------------------------")
print(prt_cmd)
print(prt_rew)
print(prt_int_rew)
print(prt_rc_rew)
print(msg)
# test on a different set of games
if run_test and (epoch + 1) % config["training"]["scheduling"][
"logging_frequency"] == 0:
valid_R, valid_IR, valid_S = test(config, valid_env, agent,
test_batch_size, word2id)
summary.add_scalar('valid_reward', valid_R, epoch + 1)
summary.add_scalar('valid_interm_reward', valid_IR, epoch + 1)
summary.add_scalar('valid_step', valid_S, epoch + 1)
# save & reload checkpoint by best valid performance
model_checkpoint_path = config['training']['scheduling'][
'model_checkpoint_path']
if valid_R > best_avg_reward or (valid_R == best_avg_reward
and valid_S < best_avg_step):
best_avg_reward = valid_R
best_avg_step = valid_S
torch.save(agent.model.state_dict(), model_checkpoint_path)
print("========= saved checkpoint =========")
for test_id in range(len(test_env_list)):
R, IR, S = test(config, test_env_list[test_id], agent,
test_batch_size, word2id)
summary.add_scalar('test_reward_' + str(test_id), R,
epoch + 1)
summary.add_scalar('test_interm_reward_' + str(test_id),
IR, epoch + 1)
summary.add_scalar('test_step_' + str(test_id), S,
epoch + 1)
def train(n_epochs,
epsilon,
gamma,
load_model,
filename,
random_opponent,
n_games_test,
freq_test,
n_skip_games=int(0),
verbose=False):
"""
Train 2 agents by making them play and learn together. Save the
learned Q-function into CSV file. It is possible to confront 1 of
the agents (against either the user or a Random Agent) during
training, as often as one wants. It is also possible to train an already
trained model.
Parameters
----------
n_epochs: int
Number of games used for training.
epsilon: float (in [0,1])
Fraction of greedy decisions during training of the 2 RL Agents.
gamma: float (in [0,1])
Factor of significance of first actions over last ones for the
2 RL Agents.
load_model: string
CSV filename in which is stored the learned Q-function of an
agent. If load_model = 'model', the function loads the model
'./Models/model.csv'. If load_model is not None, the previous
parameters epsilon and gamma are used for a second training.
filename: string
Name of the CSV file that will store the learned Q-function
of one of the agents. The path to CSV file is
then ./Models/filename.csv. The counter of state-action
pairs is also stored at ./Models/data/count_filename.csv for
future training.
random_opponent: boolean
If set to true, the function trains 1 RL Agent by making it
play against a Random Agent. Otherwise, the RL agent is
trained by playing against another version of itself.
n_games_test: int
Number of games one of the RL Agent plays against a Random Agent
for testing. If set to 0, the RL Agents will not be tested by a
Random Agent.
freq_test: int
Number of epochs after which one of the RL Agents plays n_games_test
games against a Random Agent. If set to 1000, each 1000 epochs of
training, one of the RL Agents is tested against a Random Agent.
If set to 0, test occurs at the last epoch of training only.
If set to -1, none of the agents is tested during training.
n_skip_games: int
Number of epochs after which the user can choose to play
against one of the learning agents. If set to 1000,
each 1000 games, the user can choose to play against
one agent. If set to 0, the user can choose to play against one
agent at the last epoch only. If set to -1, no choice is offered
and the user cannot test any agent.
verbose: boolean
If set to True, each game action during training has a
written explanation.
Return
------
learning_results: list
Only significant with n_games_test > 0 (otherwise, empty list
by default). List of each n_epochs // freq_test epoch test results
against a Random Agent. Each test result is a list:
[current epoch, score of RL Agent, number of finished games,
n_games test].
"""
# Learning agent
agent1 = RLAgent(epsilon, gamma)
if load_model is not None:
agent1.load_model(load_model)
# Choose opponent
if random_opponent:
agent2 = RandomAgent()
time_limit = None
print('Training vs Random')
else:
agent2 = RLAgent(epsilon, gamma)
if load_model is not None:
agent2.load_model(load_model)
time_limit = None
print('Training vs Self')
start_idx = 0
scores = [0, 0]
# If the user only confronts the agent at the last epoch
# or if no confrontation
if n_skip_games in [-1, 0]:
n_skip_games = n_epochs - n_skip_games
# Boolean for game between the user and agent1 preceding a game
# between agent1 and agent2
play_checkpoint_usr = False
# If there is a test of agent1 at the last epoch only or no test
if freq_test in [-1, 0]:
freq_test = n_epochs - freq_test
# Number of games between agent1 and a Random Agent for testing
n_games_test_mem = n_games_test
learning_results = []
# Start training
print('Training epoch:')
for epoch in range(1, n_epochs + 1):
if epoch % (n_epochs // 10) == 0:
print(epoch, '/', n_epochs)
#Update boolean for playing with user
play_checkpoint_usr = bool(epoch % n_skip_games == 0)
if play_checkpoint_usr:
# Print training status
print('Number of games: ', epoch)
print('Scores: ', scores)
# Ask user to play
play = int(input('Play ? (1 Yes | 0 No)\n'))
play_checkpoint_usr = bool(play)
# Update boolean for test
n_games_test = int(epoch % freq_test == 0) * n_games_test_mem
# Start game
game_over, winner, test_results = game_2Agents(
agent1,
agent2,
start_idx=start_idx,
train=True,
time_limit=time_limit,
n_games_test=n_games_test,
play_checkpoint_usr=play_checkpoint_usr,
verbose=verbose)
assert game_over, str('Game not over but new game' +
' beginning during training')
if winner in [0, 1]:
scores[winner] += 1
# Save test games of agent1 against a Random Agent
if bool(n_games_test):
assert len(test_results) != 0, \
'Agent1 has been tested but there is no result of that.'
learning_results.append(
[epoch, test_results[2], test_results[0], test_results[1]])
# Next round
start_idx = 1 - start_idx
# Save Q-function of agent1
np.savetxt(str('Models/' + filename + '.csv'), agent1.Q, delimiter=',')
# Save stats for learning rate of agent1
np.savetxt(str('Models/data/count_' + filename + '.csv'),
agent1.count_state_action,
delimiter=',')
return learning_results
np.savetxt(str('Models/data/count_' + filename + '.csv'),
agent1.count_state_action,
delimiter=',')
return learning_results
if __name__ == "__main__":
train(n_epochs=5000,
epsilon=0.6,
gamma=1.0,
load_model=None,
filename='greedy0_6_vsSelf_test',
random_opponent=False,
n_games_test=0,
freq_test=-1,
n_skip_games=-1,
verbose=False)
agent1 = RLAgent()
agent1.load_model('greedy0_2_vsRandomvsSelf')
agent2 = RLAgent()
agent2.load_model('greedy0_6_vsSelf_test')
results = compare_agents(agent1,
agent2,
n_games=10,
time_limit=None,
verbose=False)
print(results)
import random
import torch
from game import Game
from agent import RLAgent
from moves import Moves
game = Game()
agent = RLAgent()
moves = Moves()
num_win = 0 #initialize no. of win by human
num_lose = 0 #initialize no. of win by ai but loss by human
num_tie = 0
random.seed(1000)
def check_board_and_may_update_state_values():
global num_win, num_lose, num_tie
win_or_tie = True
if game.who_wins() == -1: #human win
print("YOU WIN!!")
agent.update_state_values(0)
num_win += 1
elif game.who_wins() == 1: #ai win
print("YOU LOSE!!")
agent.update_state_values(1)
import torch
import random
from game import Game
from agent import RLAgent
from moves import Moves
game = Game()
agent = RLAgent()
moves = Moves()
agent.state_values = torch.load('./tic_tac_toe.pth')
def check_board():
win_or_tie = True
if game.who_wins() == -1: #human win
print("YOU WIN!!")
game.clear()
elif game.who_wins() == 1: #ai win
print("YOU LOSE!!")
game.clear()
elif game.who_wins() == 2: #Tie
print("TIE!!")
game.clear()
else:
win_or_tie = False
return win_or_tie
def game_mngr():
"""
Game manager, used for navigation among different choices
offered to user.
"""
# Options
command = options('PLAY', 'RULES', 'Tap 1 to play or 2 to read the rules')
# Rules page
if int(command) == 2:
print_rules()
# Go back
print('Tap 1 to come back to the main menu\n')
comeback = tap_valid_digits([1])
if int(comeback):
game_mngr()
# Game page
if int(command) == 1:
# Options
players = options('PLAYER',
'PLAYERS',
'How many players ?',
comeback=True)
# Go back
if int(players) == 0:
game_mngr()
# 2 players
if int(players) == 2:
# Ask players' name
player1, player2 = input_names(n_players=2)
# Init scores
scores = [0, 0]
# Games
tapnswap = TapnSwap()
over = False
while not over:
game_over, winner = game_1vs1(tapnswap, player1, player2)
scores[winner] += 1
if game_over:
# Display scores
restart = display_endgame(scores, player1, player2)
# Go back
if not restart:
over = True
game_mngr()
# 1 player
if int(players) == 1:
# Options
level = options('EASY',
'DIFFICULT',
'Which level ?',
comeback=True)
# Go back
if int(level) == 0:
game_mngr()
# Define agent
elif int(level) == 1:
agent = RandomAgent() # easy
else:
# Load agent
agent = RLAgent()
agent.load_model('greedy0_2_vsRandomvsSelf') # difficult
# Ask player's name
player = input_names(n_players=1)
# Init scores
scores = [0, 0]
# Games
tapnswap = TapnSwap()
over = False
while not over:
game_over, winner = game_1vsAgent(tapnswap,
player,
agent,
greedy=False)
scores[winner] += 1
if game_over:
# Display scores
restart = display_endgame(scores, player, 'Computer')
# Go back
if not restart:
over = True
game_mngr()
args = parser.parse_args()
# create world
world = World(args.config_file, thread_num=args.thread)
# create agents
agents = []
for i in world.intersections:
action_space = gym.spaces.Discrete(len(i.phases))
agents.append(
RLAgent(
action_space,
LaneVehicleGenerator(world,
i, ["lane_count"],
in_only=True,
average="road"),
LaneVehicleGenerator(world,
i, ["lane_waiting_count"],
in_only=True,
average="all",
negative=True)))
# create metric
metric = TravelTimeMetric(world)
# create env
env = TSCEnv(world, agents, metric)
# simulate
obs = env.reset()
for i in range(args.steps):
