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, 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 CommandLineAgent
from drl_gym.environments import GridWorldGameState
from drl_gym.runners import run_to_the_end
if __name__ == "__main__":
gs = GridWorldGameState()
agent = CommandLineAgent()
print(gs)
run_to_the_end([agent], gs)
print(gs)