from agents import CommandLineAgent
from environments import GridWorldGameState
from runners import run_to_the_end
if __name__ == "__main__":
gs = GridWorldGameState()
agent = CommandLineAgent()
print(gs)
run_to_the_end([agent], gs)
print(gs)
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 RandomAgent, PPOAgent
from environments import GridWorldGameState
from runners import run_to_the_end, run_for_n_games_and_print_stats, run_step
import tensorflow as tf
if __name__ == "__main__":
tf.compat.v1.disable_eager_execution()
gs = GridWorldGameState()
agent = PPOAgent(state_space_size=gs.get_vectorized_state().shape[0],
action_space_size=gs.get_action_space_size())
for i in range(20):
print(i)
run_for_n_games_and_print_stats([agent], gs, 300)
while not gs.is_game_over():
run_step([agent], gs)
print(gs)
gs.__init__()
print(gs)
run_to_the_end([agent], gs)
print(gs)