def dqn_play_keras_rl(self):
"""Create 6 players, one of them a trained DQN"""
env_name = 'neuron_poker-v0'
stack = 500
self.env = gym.make(env_name, initial_stacks=stack, render=self.render)
self.env.add_player(
EquityPlayer(name='equity/50/50',
min_call_equity=.5,
min_bet_equity=.5))
self.env.add_player(
EquityPlayer(name='equity/50/80',
min_call_equity=.8,
min_bet_equity=.8))
self.env.add_player(
EquityPlayer(name='equity/70/70',
min_call_equity=.7,
min_bet_equity=.7))
self.env.add_player(
EquityPlayer(name='equity/20/30',
min_call_equity=.2,
min_bet_equity=.3))
self.env.add_player(RandomPlayer())
self.env.add_player(PlayerShell(name='keras-rl', stack_size=stack))
self.env.reset()
dqn = DQNPlayer(load_model='dqn1', env=self.env)
dqn.play(nb_episodes=self.num_episodes, render=self.render)
def dqn_train_keras_rl(self):
"""Implementation of kreras-rl deep q learing."""
env_name = 'neuron_poker-v0'
stack = 100
env = gym.make(env_name,
initial_stacks=stack,
funds_plot=self.funds_plot,
render=self.render,
use_cpp_montecarlo=self.use_cpp_montecarlo)
np.random.seed(123)
env.seed(123)
# env.add_player(EquityPlayer(name='equity/50/70', min_call_equity=.5, min_bet_equity=.7))
env.add_player(
EquityPlayer(name='equity/20/30',
min_call_equity=.2,
min_bet_equity=.3))
env.add_player(RandomPlayer())
# env.add_player(RandomPlayer())
# env.add_player(RandomPlayer())
env.add_player(PlayerShell(
name='keras-rl',
stack_size=stack)) # shell is used for callback to keras rl
env.reset()
dqn = DQNPlayer()
dqn.initiate_agent(env)
dqn.train(env_name='dqn1')
def dqn_play_keras_rl(self, model_name):
"""Create 6 players, one of them a trained DQN"""
from agents.agent_consider_equity import Player as EquityPlayer
from agents.agent_keras_rl_dqn import Player as DQNPlayer
from agents.agent_random import Player as RandomPlayer
env_name = 'neuron_poker-v0'
self.env = gym.make(env_name,
initial_stacks=self.stack,
render=self.render)
self.env.add_player(
EquityPlayer(name='equity/50/50',
min_call_equity=.5,
min_bet_equity=.5))
self.env.add_player(
EquityPlayer(name='equity/50/80',
min_call_equity=.8,
min_bet_equity=.8))
self.env.add_player(
EquityPlayer(name='equity/70/70',
min_call_equity=.7,
min_bet_equity=.7))
self.env.add_player(
EquityPlayer(name='equity/20/30',
min_call_equity=.2,
min_bet_equity=.3))
self.env.add_player(RandomPlayer())
self.env.add_player(PlayerShell(name='keras-rl',
stack_size=self.stack))
self.env.reset()
dqn = DQNPlayer(load_model=model_name, env=self.env)
dqn.play(nb_episodes=self.num_episodes, render=self.render)
def dqn_train_keras_rl(self, model_name):
"""Implementation of kreras-rl deep q learing."""
from agents.agent_consider_equity import Player as EquityPlayer
from agents.agent_keras_rl_dqn import Player as DQNPlayer
from agents.agent_random import Player as RandomPlayer
env_name = 'neuron_poker-v0'
env = gym.make(env_name,
initial_stacks=self.stack,
funds_plot=self.funds_plot,
render=self.render,
use_cpp_montecarlo=self.use_cpp_montecarlo)
np.random.seed(123)
env.seed(123)
# env.add_player(EquityPlayer(name='equity/50/70',
# min_call_equity=.5, min_bet_equity=.7))
# env.add_player(EquityPlayer(name='equity/20/30',
# min_call_equity=.2, min_bet_equity=.3))
# env.add_player(RandomPlayer())
# env.add_player(RandomPlayer())
# env.add_player(RandomPlayer())
# shell is used for callback to keras rl
env.add_player(
EquityPlayer(name='equity_default',
min_call_equity=.4,
min_bet_equity=.5))
env.add_player(PlayerShell(name='keras-rl', stack_size=self.stack))
env.reset()
dqn = DQNPlayer()
dqn.initiate_agent(env)
dqn.train(env_name=model_name)
def dqn_train_keras_rl(self):
"""Implementation of kreras-rl deep q learing."""
env_name = 'neuron_poker-v0'
stack = 2000
env = gym.make(env_name, initial_stacks=stack, funds_plot=self.funds_plot, render=self.render,
use_cpp_montecarlo=self.use_cpp_montecarlo)
np.random.seed(123)
env.seed(123)
# env.add_player(EquityPlayer(name='equity/50/70', min_call_equity=.5, min_bet_equity=.7))
# env.add_player(RandomPlayer())
# env.add_player(RandomPlayer())
# env.add_player(RandomPlayer())
# env.add_player(PlayerShell(name='keras-rl-1', stack_size=stack), range=0.9) # shell is used for callback to keras rl
# env.add_player(PlayerShell(name='keras-rl-2', stack_size=stack), range=0.9) # shell is used for callback to keras rl
# env.add_player(PlayerShell(name='keras-rl-3', stack_size=stack), range=0.9) # shell is used for callback to keras rl
# env.add_player(PlayerShell(name='keras-rl-4', stack_size=stack), range=0.9) # shell is used for callback to keras rl
# env.add_player(PlayerShell(name='keras-rl-5', stack_size=stack), range=0.9) # shell is used for callback to keras rl
# env.add_player(PlayerShell(name='keras-rl-6', stack_size=stack), range=0.9) # shell is used for callback to keras rl
# env.add_player(PlayerShell(name='keras-rl-7', stack_size=stack), range=0.9) # shell is used for callback to keras rl
env.add_player(PlayerShell(name='LJY', stack_size=stack, range=0.33)) # shell is used for callback to keras rl
# dqn = DQNPlayer(name='DQN-1',stack_size=2000, range=0.9, env=env , load_model=None)
# env.add_player(dqn)
env.add_player(RandomPlayer(name='Random-1',range=1))
# env.add_player(RandomPlayer(name='Random-2',range=1))
# env.add_player(RandomPlayer(name='Random-3',range=1))
# env.add_player(RandomPlayer(name='Random-4',range=1))
# env.add_player(RandomPlayer(name='Random-5',range=1))
# env.add_player(RandomPlayer(name='Random-6',range=1))
# env.add_player(RandomPlayer(name='Random-7',range=1))
# env.add_player(DQNPlayer(name='DQN-2',stack_size=2000, range=0.9, env=env , load_model=None))
# env.add_player(DQNPlayer(name='DQN-3',stack_size=2000, range=0.9, env=env , load_model=None))
# env.add_player(DQNPlayer(name='DQN-4',stack_size=2000, range=0.9, env=env , load_model=None))
# env.add_player(DQNPlayer(name='DQN-5',stack_size=2000, range=0.9, env=env , load_model=None))
# env.add_player(DQNPlayer(name='DQN-6',stack_size=2000, range=0.9, env=env , load_model=None))
# env.add_player(DQNPlayer(name='DQN-7',stack_size=2000, range=0.9, env=env , load_model=None))
# env.add_player(DQNPlayer(name='DQN-8',stack_size=2000, range=0.9, env=env , load_model=None))
env.reset()
# print(env.players[0].range)
# print(env.players[1].range)
# print(env.players[2].range)
# print(env.players[3].range)
# print(env.players[4].range)
# print(env.players[5].range)
dqn = DQNPlayer()
# dqn.initiate_agent(env,load_model='3dqn_vs_3rd')
dqn.initiate_agent(env)
dqn.train(ckpt_name='LJY')
def dqn_train_heads_up_keras_rl(self, model_name):
"""Implementation of kreras-rl deep q learing."""
from agents.agent_consider_equity import Player as EquityPlayer
from agents.agent_keras_rl_dqn import Player as DQNPlayer
from agents.agent_random import Player as RandomPlayer
env_name = 'neuron_poker-v0'
env = gym.make(env_name, initial_stacks=self.stack, funds_plot=self.funds_plot, render=self.render,
use_nn_equity=self.use_nn_equity, use_cpp_montecarlo=self.use_cpp_montecarlo)
# np.random.seed(123)
# env.seed(123)
env.add_player(EquityPlayer(name='equity/50/70', min_call_equity=.5, min_bet_equity=.7))
env.add_player(PlayerShell(name='keras-rl', stack_size=self.stack)) # shell is used for callback to keras rl
env.reset()
dqn = DQNPlayer()
# dqn.initiate_agent(env, load_memory=model_name, load_model=model_name, load_optimizer=model_name)
# # dqn.initiate_agent(env, load_memory=None, load_model=None, load_optimizer=None)
# dqn.initiate_agent(env, load_memory=None, load_model=None, load_optimizer=None, batch_size=128)
# dqn.train(env_name=model_name, policy_epsilon=0.9)
batch_sizes = [128, 128, 128, 128, 128, 128, 128, 128]
policy_epsilon = [0.1, 0.1,0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
learn_rate = np.geomspace(1e-2, 1e-4, 5)
for x in range(10):
dqn = DQNPlayer()
# dqn.initiate_agent(env, load_memory=model_name, load_model=model_name, load_optimizer=model_name, batch_size=128)
dqn.initiate_agent(env, model_name=None, load_memory=None, load_model=None, load_optimizer=None, batch_size=128, learn_rate=learn_rate[x])
dqn.train(env_name=model_name, policy_epsilon=policy_epsilon[x])