from agents import CommandLineAgent, DeepQLearningAgent, PPOAgent, RandomAgent
from environments.tictactoe import TicTacToeGameState
from 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 agents import TabularLikeDeepQLearningAgent
from environments import GridWorldGameState
from runners import run_for_n_games_and_print_stats, run_step
if __name__ == "__main__":
gs = GridWorldGameState()
agent = TabularLikeDeepQLearningAgent(action_space_size=4)
for i in range(500):
run_for_n_games_and_print_stats([agent], gs, 100)
agent.epsilon = -1.0
run_for_n_games_and_print_stats([agent], gs, 100)
gs = gs.clone()
while not gs.is_game_over():
run_step([agent], gs)
print(gs)
from agents import RandomRolloutAgent, MOMCTSAgent, RandomAgent, HalfAlphaZeroAgent
from environments.tictactoe import TicTacToeGameState
from runners import run_to_the_end, 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)
