def train_mdnrnn(
env: EnvWrapper,
trainer: MDNRNNTrainer,
trainer_preprocessor,
num_train_transitions: int,
seq_len: int,
batch_size: int,
num_train_epochs: int,
# for optional validation
test_replay_buffer=None,
):
train_replay_buffer = ReplayBuffer(
replay_capacity=num_train_transitions,
batch_size=batch_size,
stack_size=seq_len,
return_everything_as_stack=True,
)
fill_replay_buffer(env, train_replay_buffer, num_train_transitions)
num_batch_per_epoch = train_replay_buffer.size // batch_size
logger.info("Made RBs, starting to train now!")
for epoch in range(num_train_epochs):
for i in range(num_batch_per_epoch):
batch = train_replay_buffer.sample_transition_batch(
batch_size=batch_size)
preprocessed_batch = trainer_preprocessor(batch)
losses = trainer.train(preprocessed_batch)
print_mdnrnn_losses(epoch, i, losses)
# validation
if test_replay_buffer is not None:
with torch.no_grad():
trainer.memory_network.mdnrnn.eval()
test_batch = test_replay_buffer.sample_transition_batch(
batch_size=batch_size)
preprocessed_test_batch = trainer_preprocessor(test_batch)
valid_losses = trainer.get_loss(preprocessed_test_batch)
print_mdnrnn_losses(epoch, "validation", valid_losses)
trainer.memory_network.mdnrnn.train()
return trainer
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 = torch.zeros((seq_len, state_dim))
next_state_mem = torch.zeros((seq_len, state_dim))
action_mem = torch.zeros((seq_len, action_dim))
reward_mem = torch.zeros(seq_len)
not_terminal_mem = torch.zeros(seq_len)
next_mus_mem = torch.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()
action_mem[s] = action
reward_mem[s] = reward.detach()
not_terminal_mem[s] = not_terminal
next_state_mem[s] = next_state.detach()
next_mus_mem[s] = next_mus.detach()
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,
)
if use_gpu:
mdnrnn_net = mdnrnn_net.cuda()
trainer = MDNRNNTrainer(mdnrnn_network=mdnrnn_net,
params=mdnrnn_params,
cum_loss_hist=num_batch)
for e in range(num_epochs):
for i in range(num_batch):
training_batch = replay_buffer.sample_memories(
batch_size, use_gpu=use_gpu, batch_first=True)
losses = trainer.train(training_batch, batch_first=True)
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"
def train_sgd(
gym_env: OpenAIGymEnvironment,
trainer: MDNRNNTrainer,
use_gpu: bool,
test_run_name: str,
minibatch_size: int,
run_details: OpenAiRunDetails,
test_batch: rlt.PreprocessedTrainingBatch,
):
assert run_details.max_steps is not None
train_replay_buffer = get_replay_buffer(
run_details.num_train_episodes,
run_details.seq_len,
run_details.max_steps,
gym_env,
)
valid_replay_buffer = get_replay_buffer(
run_details.num_test_episodes,
run_details.seq_len,
run_details.max_steps,
gym_env,
)
valid_batch = valid_replay_buffer.sample_memories(
valid_replay_buffer.memory_size, use_gpu=use_gpu, batch_first=True)
valid_loss_history = []
num_batch_per_epoch = train_replay_buffer.memory_size // minibatch_size
logger.info(
"Collected data {} transitions.\n"
"Training will take {} epochs, with each epoch having {} mini-batches"
" and each mini-batch having {} samples".format(
train_replay_buffer.memory_size,
run_details.train_epochs,
num_batch_per_epoch,
minibatch_size,
))
for i_epoch in range(run_details.train_epochs):
for i_batch in range(num_batch_per_epoch):
training_batch = train_replay_buffer.sample_memories(
minibatch_size, use_gpu=use_gpu, batch_first=True)
losses = trainer.train(training_batch, batch_first=True)
logger.info(
"{}-th epoch, {}-th minibatch: \n"
"loss={}, bce={}, gmm={}, mse={} \n"
"cum loss={}, cum bce={}, cum gmm={}, cum mse={}\n".format(
i_epoch,
i_batch,
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),
))
trainer.mdnrnn.mdnrnn.eval()
valid_losses = trainer.get_loss(valid_batch,
state_dim=gym_env.state_dim,
batch_first=True)
valid_losses = loss_to_num(valid_losses)
valid_loss_history.append(valid_losses)
trainer.mdnrnn.mdnrnn.train()
logger.info(
"{}-th epoch, validate loss={}, bce={}, gmm={}, mse={}".format(
i_epoch,
valid_losses["loss"],
valid_losses["bce"],
valid_losses["gmm"],
valid_losses["mse"],
))
latest_loss = valid_loss_history[-1]["loss"]
recent_valid_loss_hist = valid_loss_history[-1 - run_details.
early_stopping_patience:-1]
# earlystopping
if len(valid_loss_history
) > run_details.early_stopping_patience and all(
(latest_loss >= v["loss"] for v in recent_valid_loss_hist)):
break
trainer.mdnrnn.mdnrnn.eval()
test_losses = trainer.get_loss(test_batch,
state_dim=gym_env.state_dim,
batch_first=True)
test_losses = loss_to_num(test_losses)
logger.info("Test loss: {}, bce={}, gmm={}, mse={}".format(
test_losses["loss"],
test_losses["bce"],
test_losses["gmm"],
test_losses["mse"],
))
logger.info("Valid loss history: {}".format(valid_loss_history))
return test_losses, valid_loss_history, trainer