from drl_gym.agents import CommandLineAgent, RandomAgent
from drl_gym.environments.tictactoe import TicTacToeGameState
from drl_gym.runners import run_to_the_end
if __name__ == "__main__":
gs = TicTacToeGameState()
agent0 = CommandLineAgent()
agent1 = RandomAgent()
print(gs)
run_to_the_end([agent0, agent1], gs)
print(gs)
from drl_gym.agents import RandomAgent, HalfAlphaZeroAgent
from drl_gym.environments.tictactoe import TicTacToeGameState
from drl_gym.runners import run_for_n_games_and_print_stats
if __name__ == "__main__":
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
gs = TicTacToeGameState()
agent0 = HalfAlphaZeroAgent(10,
gs.get_action_space_size(),
keep_memory=True)
agent1 = RandomAgent()
for _ in range(1000):
run_for_n_games_and_print_stats([agent0, agent1],
gs,
100,
shuffle_players=True)
from drl_gym.agents import TabQLearningAgent, CommandLineAgent
from drl_gym.environments.tictactoe import TicTacToeGameState
from drl_gym.runners import run_for_n_games_and_print_stats, run_step
if __name__ == "__main__":
gs = TicTacToeGameState()
agent0 = TabQLearningAgent()
agent1 = TabQLearningAgent()
agent0.alpha = 0.1
agent0.epsilon = 0.005
agent1.alpha = 0.1
agent1.epsilon = 0.005
for _ in range(100):
run_for_n_games_and_print_stats([agent0, agent1], gs, 5000)
agent0.epsilon = -1.0
agent1.epsilon = -1.0
run_for_n_games_and_print_stats([agent0, agent1], gs, 100)
gs_clone = gs.clone()
while not gs_clone.is_game_over():
run_step([agent0, CommandLineAgent()], gs_clone)
print(gs_clone)
gs_clone = gs.clone()
while not gs_clone.is_game_over():
run_step([CommandLineAgent(), agent1], gs_clone)
print(gs_clone)
from drl_gym.agents import CommandLineAgent, PPOAgent, RandomAgent
from drl_gym.environments.tictactoe import TicTacToeGameState
from drl_gym.runners import run_for_n_games_and_print_stats, run_step
if __name__ == "__main__":
gs = TicTacToeGameState()
agent0 = PPOAgent(
state_space_size=gs.get_vectorized_state().shape[0],
action_space_size=gs.get_action_space_size(),
)
agent1 = RandomAgent()
for i in range(100):
run_for_n_games_and_print_stats([agent0, agent1], gs, 5000)
run_for_n_games_and_print_stats([agent0, agent1], gs, 100)
gs_clone = gs.clone()
while not gs_clone.is_game_over():
run_step([agent0, CommandLineAgent()], gs_clone)
print(gs_clone)
gs_clone = gs.clone()
while not gs_clone.is_game_over():
run_step([CommandLineAgent(), agent1], gs_clone)
print(gs_clone)
from drl_gym.agents import CommandLineAgent, DeepQLearningAgent
from drl_gym.environments.tictactoe import TicTacToeGameState
from drl_gym.runners import run_for_n_games_and_print_stats, run_step
if __name__ == "__main__":
gs = TicTacToeGameState()
agent0 = DeepQLearningAgent(action_space_size=gs.get_action_space_size())
agent1 = DeepQLearningAgent(action_space_size=gs.get_action_space_size())
agent0.alpha = 0.1
agent0.epsilon = 0.005
agent1.alpha = 0.1
agent1.epsilon = 0.005
for i in range(100):
run_for_n_games_and_print_stats([agent0, agent1], gs, 5000)
agent0.epsilon = -1.0
agent1.epsilon = -1.0
run_for_n_games_and_print_stats([agent0, agent1], gs, 100)
gs_clone = gs.clone()
while not gs_clone.is_game_over():
run_step([agent0, CommandLineAgent()], gs_clone)
print(gs_clone)
gs_clone = gs.clone()
while not gs_clone.is_game_over():
run_step([CommandLineAgent(), agent1], gs_clone)
print(gs_clone)
from drl_gym.agents import RandomAgent, ExpertApprenticeAgent
from drl_gym.environments.tictactoe import TicTacToeGameState
from drl_gym.runners import run_for_n_games_and_print_stats
if __name__ == "__main__":
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
gs = TicTacToeGameState()
agent0 = ExpertApprenticeAgent(100, gs.get_action_space_size())
agent1 = RandomAgent()
for _ in range(1000):
run_for_n_games_and_print_stats([agent0, agent1], gs, 1000)