def __init__(self):
self.name = 'PreDQNAgent'
self.id = "d"
# Set up the DQN agent and load the pre-trained model
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph)
self.use_raw = False
# Config
conf = Config('environ.properties')
# Set the the number of steps for collecting normalization statistics
# and intial memory size
memory_init_size = conf.get_int('memory_init_size')
norm_step = conf.get_int('norm_step')
env = rlcard3.make('mocsar_dqn')
with self.graph.as_default():
self.agent = DQNAgent(self.sess,
scope='dqn',
action_num=env.action_num,
state_shape=env.state_shape,
replay_memory_size=20000,
replay_memory_init_size=memory_init_size,
norm_step=norm_step,
mlp_layers=[512, 512])
self.normalize(env, 1000)
self.sess.run(tf.compat.v1.global_variables_initializer())
check_point_path = os.path.join(ROOT_PATH, 'mocsar_dqn')
with self.sess.as_default():
with self.graph.as_default():
saver = tf.train.Saver(tf.model_variables())
saver.restore(self.sess,
tf.train.latest_checkpoint(check_point_path))
def init_vars(conf: Config) -> Tuple:
"""
Ge the properties from the configuration
:param conf: Mocsaár config, based on environ.propertirs
:return: evaluate_num, evaluate_every, memory_init_size, train_every, episode_num
"""
# Set the iterations numbers and how frequently we evaluate/save plot
evaluate_num = conf.get_int('evaluate_num')
evaluate_every = conf.get_int('evaluate_every')
# Set the the number of steps for collecting normalization statistics
# and intial memory size
memory_init_size = conf.get_int('memory_init_size')
train_every = conf.get_int('train_every')
episode_num = conf.get_int('episode_num')
return evaluate_num, evaluate_every, memory_init_size, train_every, episode_num
def init_environment(conf: Config, env_id: str, config: Dict = {}) -> Tuple:
"""
Initialize Mocsár envronments, and return them
:param conf: Mocsaár config, based on environ.propertirs
:param envoronment_id: Mocsár environment id, like 'mocsar'
:return: (env, eval_env)
"""
# Make environment
env = rlcard3.make(env_id=env_id, config=config)
eval_env = rlcard3.make(env_id=env_id, config=config)
# Set Nr of players and cards
env.game.set_game_params(num_players=conf.get_int('nr_players'),
num_cards=conf.get_int('nr_cards'))
eval_env.game.set_game_params(num_players=conf.get_int('nr_players'),
num_cards=conf.get_int('nr_cards'))
return env, eval_env
Author: József Varga
Date created: 4/06/2020
Compare various agents
"""
import os
from typing import List
import io
from urllib.request import urlopen
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from rlcard3.games.mocsar.agentdb import str_to_agent_dict, get_by_id
from rlcard3.utils.config_read import Config
conf = Config('environ.properties')
# PATH Const
LOG_SAVE_PRFX = conf.get_str(section='cfg.compare', key="stat_dir_path")
PNG_SAVE_PRFX = conf.get_str(section='cfg.visual', key="png_dir_path")
log_dirname = conf.get_str(section='cfg.visual', key="dir_name")
log_filename = conf.get_str(section='cfg.visual', key="file_name")
def read_data_local() -> pd.DataFrame:
csv_file_name = os.path.join(LOG_SAVE_PRFX, log_dirname, log_filename)
dfr = pd.read_csv(csv_file_name, sep=";", usecols=["cardnr", "agentid", "agentstr", "payoff"])
return dfr
def read_data_github(csv_url:str) -> pd.DataFrame:
r1 = urlopen(csv_url)
"""
An example of learning a DQN Agent on Mocsár
"""
import torch
import os
from rlcard3.agents.dqn_agent_pytorch import DQNAgent
from rlcard3.agents.random_agent import RandomAgent
from rlcard3.utils.utils import set_global_seed, tournament
from rlcard3.utils.logger import Logger
from rlcard3.utils.config_read import Config
from rlcard3.games.mocsar.util_examples import init_environment, init_vars
# Config
conf = Config('environ.properties')
# Environemtn
env, eval_env = init_environment(conf=conf,
env_id='mocsar-cfg',
config={'multi_agent_mode': True})
# parameter variables
evaluate_num, evaluate_every, memory_init_size, train_every, episode_num = init_vars(
conf=conf)
# The paths for saving the logs and learning curves
log_dir = './experiments/mocsar_dqn_ra_pytorch_result/'
# Set a global seed
set_global_seed(0)
agent = DQNAgent(scope='dqn',
action_num=env.action_num,
replay_memory_init_size=memory_init_size,
"""
Compare different set of bots
Repeat random games for defined players and sums the points received.
File name: examples/mocsar_pl_cfg_config.py
Author: József Varga
Date created: 4/01/2020
"""
import rlcard3
from rlcard3.games.mocsar.agentdb import str_to_agent_list
from rlcard3.games.mocsar.stat import MocsarStat
from rlcard3.utils.config_read import Config
conf = Config('environ.properties')
NR_GAMES = conf.get_int(section='cfg.compare', key='nr_games')
# Make environment and enable human mode
env = rlcard3.make('mocsar-cfg', config={'multi_agent_mode': True})
# Create statistics
stat = MocsarStat(game=env.game,
agents=env.model.rule_agents,
nr_of_games=NR_GAMES,
batch_name=conf.get_str(section='cfg.compare',
key='batch_name'),
log_dir=conf.get_str(section='cfg.compare',
key='stat_dir_path'))
# Register agents
agents_list = str_to_agent_list(agent_str_list=conf.get_str(section='cfg.compare', key="agent_list"))
print(f"mocsar_pl_cfg_config, Agents:{agents_list}")
def test_conf():
conf = Config('environ.properties')
memory_init_size = conf.get_int('memory_init_size')
assert 1000 == memory_init_size
"""
An example of learning a NFSP Agent on Mocsár
"""
import torch
import os
from rlcard3.games.mocsar.util_examples import init_environment, init_vars
from rlcard3.agents.nfsp_agent_pytorch import NFSPAgent
from rlcard3.agents.random_agent import RandomAgent
from rlcard3.utils.config_read import Config
from rlcard3.utils.utils import set_global_seed, tournament
from rlcard3.utils.logger import Logger
# Config
conf = Config('environ.properties')
# Environemtn
env, eval_env = init_environment(conf=conf, env_id='mocsar')
# parameter variables
evaluate_num, evaluate_every, memory_init_size, train_every, episode_num = init_vars(
conf=conf)
# The paths for saving the logs and learning curves
log_dir = './experiments/mocsar_nfsp_pytorch_result/'
# Set a global seed
set_global_seed(0)
# Set agents
agents = []
for i in range(env.player_num):
agent = NFSPAgent(scope='nfsp' + str(i),
"""
Compare different agents against random agents
File name: examples/mocsar_pl_dqn_pytorch_load_model_cfg.py
Author: József Varga
Date created: 4/14/2020
"""
import rlcard3
from rlcard3.games.mocsar.stat import MocsarStat
from rlcard3.utils.config_read import Config
from rlcard3.utils.utils import tournament
conf = Config('environ.properties')
NR_GAMES = conf.get_int(section='cfg.compare', key='nr_games')
# Make environment and enable human mode
env = rlcard3.make('mocsar-cfg', config={'multi_agent_mode': True})
# Create statistics
stat = MocsarStat(game=env.game,
agents=env.model.rule_agents,
nr_of_games=NR_GAMES,
batch_name=conf.get_str(section='cfg.compare',
key='batch_name'),
log_dir=conf.get_str(section='cfg.compare',
key='stat_dir_path'))
# Register agents
agent_str = conf.get_str(section='cfg.compare', key="agent_str")
nr_cards = conf.get_int(section='global', key='nr_cards')
agents = {agent_str: 1, "mocsar_random": 3}
''' Another example of loading a pre-trained NFSP model on Leduc Hold'em
Here, we directly load the model from model zoo
'''
import rlcard3
from rlcard3.agents.random_agent import RandomAgent
from rlcard3.utils.utils import set_global_seed, tournament
from rlcard3 import models
from rlcard3.utils.config_read import Config
# Make environment
env = rlcard3.make('mocsar')
# Get parameters
conf = Config('environ.properties')
evaluate_num = conf.get_int(section='cfg.compare', key='nr_games')
agent_str = conf.get_str(section='cfg.compare', key="agent_str")
nr_cards = conf.get_int(section='global', key='nr_cards')
# Set a global seed
#set_global_seed(0)
# Here we directly load NFSP models from /models module
dqn_agents = models.load(agent_str,
num_players=env.game.get_player_num(),
action_num=env.action_num,
state_shape=env.state_shape).agents
# Evaluate the performance. Play with random agents.
random_agent = RandomAgent(env.action_num)
env.game.set_game_params(num_players=4, num_cards=nr_cards)
env.set_agents([dqn_agents[0], random_agent, random_agent, random_agent])
File name: examples/mocsar_ln_nfsp_pytorch_ra.py
Author: József Varga
Date created: 4/07/2020
"""
import torch
import os
from rlcard3.games.mocsar.util_examples import init_environment, init_vars
from rlcard3.agents.nfsp_agent_pytorch import NFSPAgent
from rlcard3.agents.random_agent import RandomAgent
from rlcard3.utils.config_read import Config
from rlcard3.utils.utils import set_global_seed, tournament
from rlcard3.utils.logger import Logger
# Config
conf = Config('environ.properties')
# Environemtn
env, eval_env = init_environment(conf=conf, env_id='mocsar-cfg', config= {'multi_agent_mode': True})
# parameter variables
evaluate_num, evaluate_every, memory_init_size, train_every, episode_num = init_vars(conf=conf)
# The paths for saving the logs and learning curves
log_dir = './experiments/mocsar_nfsp_pytorch_ra_result/'
# Set a global seed
set_global_seed(0)
# Set agents
agent = NFSPAgent(scope='nfsp',
action_num=env.action_num,
state_shape=env.state_shape,