db09dc1fb503ab8f7de69fa23e8d38742bda8e90,ch07/lib/common.py,,calc_loss_dqn,#Any#Any#Any#Any#Any#,85

Before Change

def calc_loss_dqn(batch, net, tgt_net, gamma, cuda=False):
    states, actions, rewards, dones, next_states = unpack_batch(batch)

    states_v = Variable(torch.from_numpy(states))
    next_states_v = Variable(torch.from_numpy(next_states), volatile=True)
    actions_v = Variable(torch.from_numpy(actions))
    rewards_v = Variable(torch.from_numpy(rewards))
    done_mask = torch.ByteTensor(dones)
    if cuda:
        states_v = states_v.cuda()
        next_states_v = next_states_v.cuda()
        actions_v = actions_v.cuda()
        rewards_v = rewards_v.cuda()
        done_mask = done_mask.cuda()

    state_action_values = net(states_v).gather(1, actions_v.unsqueeze(-1)).squeeze(-1)
    next_state_values = tgt_net(next_states_v).max(1)[0]
    next_state_values[done_mask] = 0.0
    next_state_values.volatile = False

    expected_state_action_values = next_state_values * gamma + rewards_v
    return nn.MSELoss()(state_action_values, expected_state_action_values)

After Change

def calc_loss_dqn(batch, net, tgt_net, gamma, device="cpu"):
    states, actions, rewards, dones, next_states = unpack_batch(batch)

    states_v = torch.tensor(states).to(device)
    next_states_v = torch.tensor(next_states).to(device)
    actions_v = torch.tensor(actions).to(device)
    rewards_v = torch.tensor(rewards).to(device)
    done_mask = torch.ByteTensor(dones).to(device)

    state_action_values = net(states_v).gather(1, actions_v.unsqueeze(-1)).squeeze(-1)
    next_state_values = tgt_net(next_states_v).max(1)[0]
    next_state_values[done_mask] = 0.0

Instances