def _test_mdnrnn_simulate_world(self, use_gpu=False):
num_epochs = 300
num_episodes = 400
batch_size = 200
action_dim = 2
seq_len = 5
state_dim = 2
simulated_num_gaussians = 2
mdrnn_num_gaussians = 2
simulated_num_hidden_layers = 1
simulated_num_hiddens = 3
mdnrnn_num_hidden_layers = 1
mdnrnn_num_hiddens = 10
adam_lr = 0.01
replay_buffer = MDNRNNMemoryPool(max_replay_memory_size=num_episodes)
swm = SimulatedWorldModel(
action_dim=action_dim,
state_dim=state_dim,
num_gaussians=simulated_num_gaussians,
lstm_num_hidden_layers=simulated_num_hidden_layers,
lstm_num_hiddens=simulated_num_hiddens,
)
possible_actions = torch.eye(action_dim)
for _ in range(num_episodes):
cur_state_mem = np.zeros((seq_len, state_dim))
next_state_mem = np.zeros((seq_len, state_dim))
action_mem = np.zeros((seq_len, action_dim))
reward_mem = np.zeros(seq_len)
not_terminal_mem = np.zeros(seq_len)
next_mus_mem = np.zeros((seq_len, simulated_num_gaussians, state_dim))
swm.init_hidden(batch_size=1)
next_state = torch.randn((1, 1, state_dim))
for s in range(seq_len):
cur_state = next_state
action = possible_actions[np.random.randint(action_dim)].view(
1, 1, action_dim
)
next_mus, reward = swm(action, cur_state)
not_terminal = 1
if s == seq_len - 1:
not_terminal = 0
# randomly draw for next state
next_pi = torch.ones(simulated_num_gaussians) / simulated_num_gaussians
index = Categorical(next_pi).sample((1,)).long().item()
next_state = next_mus[0, 0, index].view(1, 1, state_dim)
cur_state_mem[s] = cur_state.detach().numpy()
action_mem[s] = action.numpy()
reward_mem[s] = reward.detach().numpy()
not_terminal_mem[s] = not_terminal
next_state_mem[s] = next_state.detach().numpy()
next_mus_mem[s] = next_mus.detach().numpy()
replay_buffer.insert_into_memory(
cur_state_mem, action_mem, next_state_mem, reward_mem, not_terminal_mem
)
num_batch = num_episodes // batch_size
mdnrnn_params = MDNRNNParameters(
hidden_size=mdnrnn_num_hiddens,
num_hidden_layers=mdnrnn_num_hidden_layers,
minibatch_size=batch_size,
learning_rate=adam_lr,
num_gaussians=mdrnn_num_gaussians,
)
mdnrnn_net = MemoryNetwork(
state_dim=state_dim,
action_dim=action_dim,
num_hiddens=mdnrnn_params.hidden_size,
num_hidden_layers=mdnrnn_params.num_hidden_layers,
num_gaussians=mdnrnn_params.num_gaussians,
)
trainer = MDNRNNTrainer(
mdnrnn_network=mdnrnn_net,
params=mdnrnn_params,
cum_loss_hist=num_batch,
use_gpu=use_gpu,
)
for e in range(num_epochs):
for i in range(num_batch):
training_batch = replay_buffer.sample_memories(
batch_size, batch_first=use_gpu
)
losses = trainer.train(training_batch, batch_first=use_gpu)
logger.info(
"{}-th epoch, {}-th minibatch: \n"
"loss={}, bce={}, gmm={}, mse={} \n"
"cum loss={}, cum bce={}, cum gmm={}, cum mse={}\n".format(
e,
i,
losses["loss"],
losses["bce"],
losses["gmm"],
losses["mse"],
np.mean(trainer.cum_loss),
np.mean(trainer.cum_bce),
np.mean(trainer.cum_gmm),
np.mean(trainer.cum_mse),
)
)
if (
np.mean(trainer.cum_loss) < 0
and np.mean(trainer.cum_gmm) < -3.0
and np.mean(trainer.cum_bce) < 0.6
and np.mean(trainer.cum_mse) < 0.2
):
return
assert False, "losses not reduced significantly during training"