def load_agent(path):
# possible actions are
# move[-1, 1],
# strafe[-1, 1]
# pitch[-1, 1]
# turn[-1, 1]
# jump 0/1
# discreet actions
# "move -0.5" "jump_forward",
action_names = ["turn 0.15", "turn -0.15", "turn 0.01",
"turn 0.01", 'pitch 0.1', 'pitch -0.1',
'pitch 0.01', 'pitch -0.01']
actionSet = [network.CategoricalAction(action_names)]
policy_net = network.QVisualNetwork(actionSet, 2, 20, n_channels=3, activation=nn.ReLU(), batchnorm=True)
target_net = network.QVisualNetwork(actionSet, 2, 20, n_channels=3, activation=nn.ReLU(), batchnorm=True)
batch_size = 20
my_simple_agent = network.DQN(policy_net, target_net, 0.9, batch_size, 450, capacity=2000)
location = 'cuda' if torch.cuda.is_available() else 'cpu'
if os.path.exists(path):
logging.info('loading model from %s', path)
data = torch.load(path, map_location=location)
my_simple_agent.load_state_dict(data, strict=False)
return my_simple_agent.to(location)
def load_agent(path):
# possible actions are
# move[-1, 1],
# strafe[-1, 1]
# pitch[-1, 1]
# turn[-1, 1]
# jump 0/1
# for example:
# actionSet = [network.ContiniousAction('move', -1, 1),
# network.ContiniousAction('strafe', -1, 1),
# network.ContiniousAction('pitch', -1, 1),
# network.ContiniousAction('turn', -1, 1),
# network.BinaryAction('jump')]
# discreet actions
action_names = ["turn 0.1", "turn -0.1", "move 0.9", "jump_forward" ]
actionSet = [network.CategoricalAction(action_names)]
policy_net = QVisualNetworkV2(3, actionSet, 0, 41, n_channels=3, activation=nn.LeakyReLU(), batchnorm=False, num=256)
target_net = QVisualNetworkV2(3, actionSet, 0, 41, n_channels=3, activation=nn.LeakyReLU(), batchnorm=False, num=256)
batch_size = 18
transformer = common.make_noisy_transformers()
my_simple_agent = network.DQN(policy_net, target_net, 0.99, batch_size, 450, capacity=7000, transform=transformer)
location = 'cuda' if torch.cuda.is_available() else 'cpu'
if os.path.exists(path):
logging.info('loading model from %s', path)
data = torch.load(path, map_location=location)
my_simple_agent.load_state_dict(data, strict=False)
return my_simple_agent.to(location)
def load_agent(path):
# possible actions are
# move[-1, 1],
# strafe[-1, 1]
# pitch[-1, 1]
# turn[-1, 1]
# jump 0/1
# discreet actions
# "move -0.5" "jump_forward",
action_names = ["turn 0.15", "turn -0.15", "turn 0.01",
"turn 0.01", 'pitch 0.1', 'pitch -0.1',
'pitch 0.01', 'pitch -0.01']
actionSet = [network.CategoricalAction(action_names)]
transformer = common.make_noisy_transformers()
policy_net = QVisualNetworkTree(1, actionSet, 0, 34, n_channels=3, activation=nn.LeakyReLU(), batchnorm=False, num=256)
target_net = QVisualNetworkTree(1, actionSet, 0, 34, n_channels=3, activation=nn.LeakyReLU(), batchnorm=False, num=256)
batch_size = 20
my_simple_agent = network.DQN(policy_net, target_net, 0.9,
batch_size, 450, capacity=2000,
transform=transformer)
if os.path.exists('agent_tree.pth'):
location = 'cuda' if torch.cuda.is_available() else 'cpu'
logging.info('loading model from agent_tree.pth')
data = torch.load('agent_tree.pth', map_location=location)
my_simple_agent.load_state_dict(data, strict=False)
return my_simple_agent
def load_agent(path):
# possible actions are
# move[-1, 1],
# strafe[-1, 1]
# pitch[-1, 1]
# turn[-1, 1]
# jump 0/1
# discreet actions
# "move -0.5" "jump_forward",
action_names = [
"turn 0.20", "turn -0.20", "turn 0.01", "turn 0.01", 'pitch 0.1',
'pitch -0.1', 'pitch 0.01', 'pitch -0.01'
]
actionSet = [network.CategoricalAction(action_names)]
n_out = len(common.visible_blocks) + 1
location = 'cuda' if torch.cuda.is_available() else 'cpu'
net = GoodPoint(8, n_out, n_channels=3, depth=False)
model_weights = torch.load('goodpoint.pt', map_location=location)['model']
net.load_checkpoint(model_weights)
net.to(location)
policy_net = SearchTree(actionSet,
2,
n_channels=3,
activation=nn.LeakyReLU(),
block_net=net)
target_net = SearchTree(actionSet,
2,
n_channels=3,
activation=nn.LeakyReLU(),
block_net=net)
batch_size = 20
my_simple_agent = network.DQN(policy_net,
target_net,
0.9,
batch_size,
450,
capacity=2000)
if os.path.exists(path):
logging.info('loading model from ' + path)
data = torch.load(path, map_location=location)
my_simple_agent.load_state_dict(data, strict=False)
return my_simple_agent
def load_agent(path):
# possible actions are
# move[-1, 1],
# strafe[-1, 1]
# pitch[-1, 1]
# turn[-1, 1]
# jump 0/1
# for example:
# actionSet = [network.ContiniousAction('move', -1, 1),
# network.ContiniousAction('strafe', -1, 1),
# network.ContiniousAction('pitch', -1, 1),
# network.ContiniousAction('turn', -1, 1),
# network.BinaryAction('jump')]
# discreet actions
action_names = ["turn 0.15", "turn -0.15", "move 0.5", "jump_forward"]
actionSet = [network.CategoricalAction(action_names)]
policy_net = network.QNetwork(actionSet,
grid_len=27,
grid_w=5,
target_enc_len=3,
pos_enc_len=5)
target_net = network.QNetwork(actionSet,
grid_len=27,
grid_w=5,
target_enc_len=3,
pos_enc_len=5)
my_simple_agent = network.DQN(policy_net,
target_net,
0.9,
70,
450,
capacity=2000)
if os.path.exists(path):
data = torch.load(path)
my_simple_agent.load_state_dict(data, strict=False)
return my_simple_agent