if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
#device = torch.device('cpu')
print(device)
env = make_env(seed)
state_shape = env.observation_space.shape
n_actions = env.action_space.n
state = env.reset()
agent = DQNAgent(state_shape, n_actions, epsilon=0.9).to(device)
#agent.load_state_dict(torch.load('dqn.weights'))
target_network = DQNAgent(state_shape, n_actions).to(device)
target_network.load_state_dict(agent.state_dict())
opt = torch.optim.Adam(agent.parameters(), lr=1e-4)
exp_replay = ReplayBuffer(buffer_size)
print('test_buffer')
for i in range(100):
play_and_record(state, agent, env, exp_replay, n_steps=10**2)
if len(exp_replay) == buffer_size:
break
print(len(exp_replay))
state = env.reset()
for step in trange(step, total_steps + 1):
agent.epsilon = linear_decay(init_epsilon, final_epsilon, step,
decay_steps)
