def create_trainer(
params: OpenAiGymParameters, env: OpenAIGymEnvironment, use_gpu: bool
):
assert params.mdnrnn is not None
assert params.run_details.max_steps is not None
mdnrnn_params = params.mdnrnn
mdnrnn_net = MemoryNetwork(
state_dim=env.state_dim,
action_dim=env.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()
cum_loss_hist_len = (
params.run_details.num_train_episodes
* params.run_details.max_steps
// mdnrnn_params.minibatch_size
)
trainer = MDNRNNTrainer(
mdnrnn_network=mdnrnn_net, params=mdnrnn_params, cum_loss_hist=cum_loss_hist_len
)
return trainer
def create_world_model_trainer(
env: OpenAIGymEnvironment, mdnrnn_params: MDNRNNParameters, use_gpu: bool
) -> MDNRNNTrainer:
mdnrnn_net = MemoryNetwork(
state_dim=env.state_dim,
action_dim=env.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()
mdnrnn_trainer = MDNRNNTrainer(mdnrnn_network=mdnrnn_net, params=mdnrnn_params)
return mdnrnn_trainer
def create_trainer(params: Dict, env: OpenAIGymEnvironment, use_gpu: bool):
mdnrnn_params = MDNRNNParameters(**params["mdnrnn"])
mdnrnn_net = MemoryNetwork(
state_dim=env.state_dim,
action_dim=env.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 and torch.cuda.is_available():
mdnrnn_net = mdnrnn_net.cuda()
cum_loss_hist_len = (params["run_details"]["num_train_episodes"] *
params["run_details"]["max_steps"] //
mdnrnn_params.minibatch_size)
trainer = MDNRNNTrainer(mdnrnn_network=mdnrnn_net,
params=mdnrnn_params,
cum_loss_hist=cum_loss_hist_len)
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"