def __init__(self, name, action_space, cfg, shared_objects={}):
self.name = name
self.pidx = 0
self.actions_no = len(action_space)
self.target_update_freq = 400
self.batch_size = batch_size = cfg.general.batch_size
self.update_freq = batch_size
self.hist_len = 1
self.cuda = cfg.general.use_cuda
self.torch_obs = True
self.dtype = TorchTypes(self.cuda)
self.clip_grad = 0.25
self.hidden_size = 256
self.max_reward = 0
self.net = net = Policy(cfg, self.hidden_size)
if self.cuda:
net.cuda()
self.target = get_model(cfg.model.name)(cfg.model)
if self.cuda:
self.net.cuda()
self.target.cuda()
self.exploration_strategy = get_batch_schedule(cfg.agent.exploration,
batch_size)
self.batch_games = 0
self.step_cnt = 0
self.saved_transitions = []
self._o, self._a, self._v = None, None, None
self.live_idx = torch.linspace(0, batch_size-1, batch_size) \
.type(self.dtype.LongTensor)
self.lr = .001
self.gamma = .99
self.optimizer = optim.Adam(self.net.parameters(), lr=self.lr)
self.optimizer.zero_grad()
self.hidden_state = self.net.init_hidden(self.batch_size)
self.obs_select = torch.LongTensor(
[0, 1, 2, 4, 5, 6, 7, 9, 10, 11, 12, 14, 15, 16,
17]).type(self.dtype.LongTensor)
def __init__(self, name, action_space, cfg, shared_objects={}):
super(BetaDQNBatchAgent, self).__init__()
self.name = name
self.pidx = 0
self.actions_no = len(action_space)
self.target_update_freq = cfg.training.target_update
self.batch_size = batch_size = cfg.general.batch_size
self.update_freq = batch_size
self.hist_len = cfg.model.hist_len
self.cuda = cfg.general.use_cuda
self.torch_obs = True
self.dtype = TorchTypes(self.cuda)
if "model" in shared_objects:
self.net = net = shared_objects["model"]
self.print_info("Training some shared model.")
else:
self.net = net = get_model(cfg.model.name)(cfg.model)
if self.cuda:
net.cuda()
self.target = get_model(cfg.model.name)(cfg.model)
if self.cuda:
self.net.cuda()
self.target.cuda()
self.behaviour = DeterministicPolicy(self.net)
self.exploration_strategy = get_batch_schedule(cfg.agent.exploration,
batch_size)
self.ddqn = cfg.agent.ddqn
self.algorithm = DQNPolicyImprovement(self.net, self.target, cfg,
self.ddqn)
self.batch = []
self.history = []
self.batch_games = 0
self.step_cnt = 0
self._o = None
self._a = None
self.live_idx = torch.linspace(0, batch_size-1, batch_size) \
.type(self.dtype.LongTensor)
def __init__(self, name, action_space, cfg, shared_objects={}):
super(Village_ActorCritic, self).__init__()
self.name = name
self.pidx = 0
self.actions_no = len(action_space)
self.target_update_freq = 400
self.batch_size = batch_size = cfg.general.batch_size
self.update_freq = batch_size
self.hist_len = 1
self.cuda = cfg.general.use_cuda
self.torch_obs = True
self.dtype = TorchTypes(self.cuda)
#TODO move to
self.net = net = Policy(cfg)
if self.cuda:
net.cuda()
self.target = get_model(cfg.model.name)(cfg.model)
if self.cuda:
self.net.cuda()
self.target.cuda()
self.exploration_strategy = get_batch_schedule(cfg.agent.exploration,
batch_size)
self.batch_games = 0
self.step_cnt = 0
self.saved_transitions = []
self._o, self._a, self._v = None, None, None
self.live_idx = torch.linspace(0, batch_size-1, batch_size) \
.type(self.dtype.LongTensor)
self.lr = .001
self.gamma = cfg.agent.gamma
self.optimizer = optim.Adam(self.net.parameters(), lr=self.lr)
self.optimizer.zero_grad()
def __init__(self, name, action_space, cfg, shared_objects={}):
super(EpisodicRDQNAgent, self).__init__()
self.name = name
self.actions_no = len(action_space)
self.batch_size = batch_size = cfg.general.batch_size
self.cuda = cfg.general.use_cuda
self.dtype = TorchTypes(self.cuda)
if "model" in shared_objects:
self.net = net = shared_objects["model"]
self.print_info("Training some shared model.")
else:
self.net = net = get_model(cfg.model.name)(cfg.model)
if self.cuda:
net.cuda()
Optimizer = getattr(optim, cfg.training.algorithm)
optim_args = vars(cfg.training.algorithm_args)
self.optimizer = Optimizer(net.parameters(), **optim_args)
self.losses = []
self.exploration_strategy = get_batch_schedule(cfg.agent.exploration,
batch_size)
self.last_q = None
self.last_a = None
self.last_aux = None
self.live_idx = torch.linspace(0, batch_size-1, batch_size) \
.type(self.dtype.LongTensor)
self.prev_state = {}
self.crt_step = -1
self.loss_coeff = {k: v for [k,v] in cfg.model.auxiliary_tasks}
import torch
from methods import get_batch_schedule
partition = [["constant", 0.2, [.5]], ["constant", 0.2, [0.1]],
["linear", 0.6, [1.0, 0.005, 100]]]
sch = get_batch_schedule(partition, 20)
for x in sch:
print(x)
print(torch.bernoulli(x).long().nonzero().squeeze(1))
input("Push!")