def __init__(self,history_length=1):
# TODO: Define network, loss function, optimizer
# self.net = CNN(...)
self.net = CNN(history_length).to(device)
self.optimizer = torch.optim.Adam(self.net.parameters(), lr=1e-4)
# self.optimizer = torch.optim.SGD(self.net.parameters(),lr=conf.lr,momentum = 0.9)
self.loss_func = torch.nn.CrossEntropyLoss().to(device)
def __init__(self, lr=1e-4, history_length=1):
# TODO: Define network, loss function, optimizer
# self.net = CNN(...)
self.net = CNN(history_length=history_length, n_classes=5).cuda()
self.history_length = history_length
self.criterion = nn.CrossEntropyLoss().cuda()
self.optimizer = torch.optim.Adam(self.net.parameters(), lr=lr)
def __init__(self, history_length=1):
# TODO: Define network, loss function, optimizer
# self.net = CNN(...)
self.learning_rate = 1e-4
self.net = CNN(history_length = history_length).cuda()
self.loss = torch.nn.CrossEntropyLoss()
self.optimizer = torch.optim.Adam(self.net.parameters(), lr= self.learning_rate)
def __init__(self, history_size, n_actions=5, lr=0.0004):
# TODO: Define network, loss function, optimizer
# self.net = CNN(...)
self.history_size = history_size
self.num_actions = n_actions
self.net = CNN(self.history_size, n_actions).cuda()
self.lr = lr
self.criterion = torch.nn.CrossEntropyLoss()
self.optimizer = torch.optim.Adam(self.net.parameters(), lr=lr)
def __init__(self, device='cpu', history_length=1, lr=1e-4, n_classes=5):
# TODO: Define network, loss function, optimizer
self.device = torch.device(device)
self.net = CNN(history_length=history_length, n_classes=n_classes)
self.net.to(self.device)
self.lossfn = torch.nn.CrossEntropyLoss()
self.optimizer = torch.optim.Adam(self.net.parameters(), lr=lr)
def __init__(self, history_length, learning_rate, weights_classes):
weights_classes = None if weights_classes is None else weights_classes.to(
DEVICE)
self.net = CNN(history_length=history_length, n_classes=4)
# self.net = Resnet18(history_length=history_length, n_classes=4)
self.criterion = nn.CrossEntropyLoss(weight=weights_classes)
self.optimizer = torch.optim.Adam(params=self.net.parameters(),
lr=learning_rate)
# self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer, threshold=0.00001)
self.lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
self.optimizer, T_0=1, T_mult=3)
def __init__(self, network_type, lr, hidden_layers):
# TODO: Define network, loss function, optimizer
# self.net = FCN(...) or CNN(...)
if network_type == "FCN":
self.net = FCN(hidden_layers).to(device)
else:
self.net = CNN().to(device)
self.loss_fcn = nn.CrossEntropyLoss()
self.optimizer = optim.Adam(self.net.parameters(), lr)
agent.save(os.path.join(model_dir, f"dqn_agent_1.pt"))
print(f"episode: {i+1}, total reward: {episode_reward}")
max_timesteps = min(max_timesteps + 20, 1500)
tensorboard.close_session()
return training, validation
if __name__ == "__main__":
num_eval_episodes = 5
eval_cycle = 20
num_actions = 5
env = gym.make('CarRacing-v0').unwrapped
Q = CNN(n_classes=5)
Q_target = CNN(n_classes=5)
agent = DQNAgentCar(Q,
Q_target,
num_actions,
gamma=0.9,
batch_size=20,
epsilon=0.9,
tau=0.01,
lr=0.001,
history_length=0)
training, validation = train_online(env,
agent,
num_episodes=420,
history_length=0,
default=500,
required=False)
parser.add_argument("-r",
"--render",
action='store_true',
help="render during training and evaluation",
default=False,
required=False)
args = parser.parse_args()
print(args)
env = gym.make('CarRacing-v0').unwrapped
# TODO: Define Q network, target network and DQN agent
# ...
Q_network = CNN(history_length=5, n_classes=5)
Q_target = CNN(history_length=5, n_classes=5)
agent = DQNAgent(Q=Q_network,
Q_target=Q_target,
num_actions=5,
buffer_size=1e5,
lr=1e-4)
train_online(env,
agent,
num_episodes=args.episodes,
history_length=5,
model_dir="./models_carracing",
eval_cycle=20,
num_eval_episodes=5,
skip_frames=5,
return rgb2gray(state).reshape(96, 96) / 255.0
if __name__ == "__main__":
num_eval_episodes = 5
eval_cycle = 20
hist = 3
num_actions = 5
env = gym.make('CarRacing-v0').unwrapped
# TODO: Define Q network, target network and DQN agent
# ...
hist = 3
num_actions = 5
Q_target = CNN(hist + 1, num_actions)
Q = CNN(hist + 1, num_actions)
# 2. init DQNAgent (see dqn/dqn_agent.py)
agent = DQNAgent(Q,
Q_target,
num_actions,
double=False,
history_length=1e6)
# agent = DQNAgent(Q, Q_target, num_actions, double=False, epsilon = 0.99, eps_decay = True, history_length=1e6)
# 3. train DQN agent with train_online(...)
train_online(env,
agent,
num_episodes=1000,
history_length=hist,
model_dir="./models_carracing")