def api_basic_agent_play(game_type):
gs = get_gs(game_type)
agent = get_agent(game_type)
position = request.args.get("position")
player_agent.action = int(position)
run_step([player_agent, agent], gs)
run_step([player_agent, agent], gs)
data = {
"status": 200,
"message": player_agent.message,
"game": "basic",
"position_played": position
}
return jsonify(data)
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 CommandLineAgent, DeepQLearningAgent, RandomRolloutAgent
from environments.connect4 import Connect4GameState
from runners import run_to_the_end, run_for_n_games_and_print_stats, run_step
if __name__ == "__main__":
gs = Connect4GameState()
agent0 = DeepQLearningAgent(action_space_size=gs.get_action_space_size(),
neurons_per_hidden_layer=128,
hidden_layers=5)
agent1 = RandomRolloutAgent(100, False)
agent0.alpha = 0.1
agent0.epsilon = 0.3
for i in range(100):
run_for_n_games_and_print_stats([agent0, agent1], gs, 100)
agent0.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)
