def __init__(
self,
evaluator,
epsilon,
env_gen,
optim=None,
memory_queue=None,
memory_size=20000,
mem_type=None,
batch_size=64,
gamma=0.99,
):
self.epsilon = epsilon
self._epsilon = epsilon # Backup for evaluatoin
self.optim = optim
self.env = env_gen()
self.memory_queue = memory_queue
self.batch_size = batch_size
self.gamma = gamma
self.policy_net = copy.deepcopy(evaluator)
self.target_net = copy.deepcopy(evaluator)
if mem_type == "sumtree":
self.memory = WeightedMemory(memory_size)
else:
self.memory = Memory(memory_size)
def __init__(
self,
env,
evaluator,
lr=0.01,
gamma=0.99,
momentum=0.9,
weight_decay=0.01,
mem_type="sumtree",
buffer_size=20000,
batch_size=16,
*args,
**kwargs
):
self.gamma = gamma
self.env = env
self.state_size = self.env.width * self.env.height
self.policy_net = copy.deepcopy(evaluator)
# ConvNetConnect4(self.env.width, self.env.height, self.env.action_space.n).to(device)
self.target_net = copy.deepcopy(evaluator)
# ConvNetConnect4(self.env.width, self.env.height, self.env.action_space.n).to(device)
self.policy_net.apply(init_weights)
self.target_net.apply(init_weights)
self.optim = torch.optim.SGD(self.policy_net.parameters(), weight_decay=weight_decay, momentum=momentum, lr=lr,)
if mem_type == "sumtree":
self.memory = WeightedMemory(buffer_size)
else:
self.memory = Memory(buffer_size)
self.batch_size = batch_size
def __init__(
self,
evaluator,
env_gen,
optim=None,
memory_queue=None,
iterations=100,
temperature_cutoff=5,
batch_size=64,
memory_size=200000,
min_memory=20000,
update_nn=True,
starting_state_dict=None,
):
self.iterations = iterations
self.evaluator = evaluator.to(device)
self.env_gen = env_gen
self.optim = optim
self.env = env_gen()
self.root_node = None
self.reset()
self.update_nn = update_nn
self.starting_state_dict = starting_state_dict
self.memory_queue = memory_queue
self.temp_memory = []
self.memory = Memory(memory_size)
self.min_memory = min_memory
self.temperature_cutoff = temperature_cutoff
self.actions = self.env.action_space.n
self.evaluating = False
self.batch_size = batch_size
if APEX_AVAILABLE:
opt_level = "O1"
if self.optim:
self.evaluator, self.optim = amp.initialize(
evaluator, optim, opt_level=opt_level)
print("updating optimizer and evaluator")
else:
self.evaluator = amp.initialize(evaluator, opt_level=opt_level)
print(" updated evaluator")
self.amp_state_dict = amp.state_dict()
print(vars(amp._amp_state))
elif APEX_AVAILABLE:
opt_level = "O1"
print(vars(amp._amp_state))
if self.starting_state_dict:
print("laoding [sic] state dict in mcts")
self.load_state_dict(self.starting_state_dict)
def __init__(self, env=Connect4Env):
path = os.path.dirname(__file__)
self.model_shelf = shelve.open(os.path.join(path, self.MODEL_SAVE_FILE))
self.result_shelf = shelve.open(os.path.join(path, self.RESULT_SAVE_FILE))
self.elo_value_shelf = shelve.open(os.path.join(path, self.ELO_VALUE_SAVE_FILE))
self.env = env
self.memory = Memory()
atexit.register(self._close)