def run(args):
if args.train_dqn:
env_name = args.env_name or 'BreakoutNoFrameskip-v4'
env = Environment(env_name, args, atari_wrapper=True)
from agent_dqn import Agent_DQN
agent = Agent_DQN(env, args)
agent.train()
if args.test_dqn:
env = Environment('BreakoutNoFrameskip-v4', args, atari_wrapper=True, test=True)
from agent_dqn import Agent_DQN
agent = Agent_DQN(env, args)
test(agent, env, total_episodes=100)
def run(args):
if args.train_dqn:
env_name = args.env_name or 'SEVN-Test-AllObs-Shaped-v1'
env = gym.make(env_name)
from agent_dqn import Agent_DQN
agent = Agent_DQN(env, args)
print("Device: ", device)
print("n_heads: ", args.n_heads)
agent.train()
if args.test_dqn:
current_time = '2020-12-22-00:03:03'
env_name = args.env_name or 'SEVN-Test-AllObs-Shaped-v1'
env = gym.make(env_name)
from agent_dqn import Agent_DQN
agent = Agent_DQN(env, args)
test(agent, env, 10, current_time)
def run(args):
if args.train_dqn:
env_name = args.env_name or 'BreakoutNoFrameskip-v4'
env = Environment(env_name, args, atari_wrapper=True)
torch.set_default_tensor_type('torch.cuda.FloatTensor')
from agent_dqn import Agent_DQN
agent = Agent_DQN(env, args)
agent.train()
if args.test_dqn:
env = Environment('BreakoutNoFrameskip-v4',
args,
atari_wrapper=True,
test=True)
from agent_dqn import Agent_DQN
agent = Agent_DQN(env, args)
test(agent, env, total_episodes=100)
args = parser.parse_args()
return args
args = parse()
env = Environment('BreakoutNoFrameskip-v4', "", atari_wrapper=True, test=False)
n = env.action_space
state = env.reset()
device = torch.device("cpu")
input = torch.tensor(state, device=device)
agent = Agent_DQN(env, args)
dqn = DQN()
torch.save(dqn.state_dict(), "checkpoint.pth")
state_dict = torch.load("checkpoint.pth")
agent.train()
# Experience = namedtuple(
# 'Experience',
# ('state','action','next_state','reward')
# )
# e = Experience(state,action,next_state,reward)
# print(e)
# agent.train()
# print("shape:",input.shape)
#
# input = input.unsqueeze(0).to(device)
# input = input.permute(0, 3, 1, 2)
# input = input.to(device=device, dtype=torch.float)
#
# print("shape:",input.shape)