if gui_active:
win.updateSprites(state)
win.refresh()
if verbose > 0:
state.printGrid(game.grid_size)
return state
if __name__ == "__main__":
if len(sys.argv) > 1:
max_iter = int(sys.argv[1])
else:
max_iter = None
minimax_agent = MinimaxAgent(depth=lambda s,a: 2)
alphabeta_agent = AlphaBetaAgent(depth=lambda s,a: survivorDfunc(s, a, 4, 0.5), evalFn=greedyEvaluationFunction)
expectimax_agent = ExpectimaxAgent(depth=lambda s,a: cowardCenterDepthFunction(s, a, 2), evalFn=greedyEvaluationFunction)
strategies = [smartGreedyStrategy, opportunistStrategy, alphabeta_agent.getAction]
# add a human player
# strategies = [humanStrategy, smartGreedyStrategy, opportunistStrategy, alphabeta_agent.getAction]
# add an RL agent
featureExtractor = FeatureExtractor(len(strategies), grid_size = 20, radius_ = 10)
rlStrategy = load_rl_strategy("nn-nn1-r10-1b.p", strategies, featureExtractor, discount = 0.9, q_type = "nn")
strategies.append(rlStrategy)
controller(strategies, 20, max_iter = max_iter, gui_active = True, verbose = 0, game_speed = 10)
state.printGrid(game.grid_size)
return state
if __name__ == "__main__":
human_player = False
if len(sys.argv) > 1 and sys.argv[1] == "h":
human_player = True
max_iter = None
minimax_agent = searchAgent("minimax", depth=lambda s, a: 2)
alphabeta_agent = searchAgent(
"alphabeta",
depth=lambda s, a: survivorDfunc(s, a, 4, 0.5),
evalFn=greedyEvaluationFunction)
expectimax_agent = searchAgent(
"expectimax",
depth=lambda s, a: cowardCenterDepthFunction(s, a, 2),
evalFn=greedyEvaluationFunction)
strategies = [alphabeta_agent]
# strategies = [SmartGreedyAgent, OpportunistAgent]
# strategies = [SmartGreedyAgent, OpportunistAgent, alphabeta_agent]
# add an RL agent
rl_hp = load_from(config.filename + ".p")
# rl_hp = load_from("rl-ql-linear-r6-1000.p")
featureExtractor = FeatureExtractor(len(strategies),
grid_size=20,
win.refresh()
if verbose > 0:
state.printGrid(game.grid_size)
return state
if __name__ == "__main__":
human_player = False
if len(sys.argv) > 1 and sys.argv[1] == "h":
human_player = True
max_iter = None
minimax_agent = searchAgent("minimax", depth = lambda s,a : 2)
alphabeta_agent = searchAgent("alphabeta", depth = lambda s,a : survivorDfunc(s, a, 4, 0.5), evalFn = greedyEvaluationFunction)
expectimax_agent = searchAgent("expectimax", depth = lambda s,a : cowardCenterDepthFunction(s, a, 2), evalFn = greedyEvaluationFunction)
# strategies = [SmartGreedyAgent, OpportunistAgent]
strategies = [SmartGreedyAgent, OpportunistAgent, alphabeta_agent]
# add an RL agent
rl_hp = load_from(config.filename + ".p")
# rl_hp = load_from("rl-ql-linear-r6-1000.p")
featureExtractor = FeatureExtractor(len(strategies), grid_size = 20, radius_ = rl_hp.radius)
rlStrategy = load_rl_strategy(rl_hp, strategies, featureExtractor)
# rlStrategy = load_rl_strategy(rl_hp, strategies, featureExtractor)
# esStrategy = load_es_strategy("es-linear-r4-50.p", strategies, featureExtractor, discount = 0.9)
strategies.append(rlStrategy)