Source code for ding.reward_model.trex_reward_model
from copy import deepcopy
from typing import Tuple, Optional, List, Dict
from easydict import EasyDict
import pickle
import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from ding.utils import REWARD_MODEL_REGISTRY
from ding.utils import SequenceType
from ding.model.common import FCEncoder
from ding.utils import build_logger
from ding.utils.data import default_collate
from .base_reward_model import BaseRewardModel
from .rnd_reward_model import collect_states
class TrexConvEncoder(nn.Module):
r"""
Overview:
The ``Convolution Encoder`` used in models. Used to encoder raw 2-dim observation.
Interfaces:
``__init__``, ``forward``
"""
def __init__(
self,
obs_shape: SequenceType,
hidden_size_list: SequenceType = [16, 16, 16, 16, 64, 1],
activation: Optional[nn.Module] = nn.LeakyReLU()
) -> None:
r"""
Overview:
Init the Trex Convolution Encoder according to arguments. TrexConvEncoder is different \
from the ConvEncoder in model.common.encoder, their stride and kernel size parameters \
are different
Arguments:
- obs_shape (:obj:`SequenceType`): Sequence of ``in_channel``, some ``output size``
- hidden_size_list (:obj:`SequenceType`): The collection of ``hidden_size``
- activation (:obj:`nn.Module`):
The type of activation to use in the conv ``layers``,
if ``None`` then default set to ``nn.LeakyReLU()``
"""
super(TrexConvEncoder, self).__init__()
self.obs_shape = obs_shape
self.act = activation
self.hidden_size_list = hidden_size_list
layers = []
kernel_size = [7, 5, 3, 3]
stride = [3, 2, 1, 1]
input_size = obs_shape[0] # in_channel
for i in range(len(kernel_size)):
layers.append(nn.Conv2d(input_size, hidden_size_list[i], kernel_size[i], stride[i]))
layers.append(self.act)
input_size = hidden_size_list[i]
layers.append(nn.Flatten())
self.main = nn.Sequential(*layers)
flatten_size = self._get_flatten_size()
self.mid = nn.Sequential(
nn.Linear(flatten_size, hidden_size_list[-2]), self.act,
nn.Linear(hidden_size_list[-2], hidden_size_list[-1])
)
def _get_flatten_size(self) -> int:
r"""
Overview:
Get the encoding size after ``self.main`` to get the number of ``in-features`` to feed to ``nn.Linear``.
Arguments:
- x (:obj:`torch.Tensor`): Encoded Tensor after ``self.main``
Returns:
- outputs (:obj:`torch.Tensor`): Size int, also number of in-feature
"""
test_data = torch.randn(1, *self.obs_shape)
with torch.no_grad():
output = self.main(test_data)
return output.shape[1]
def forward(self, x: torch.Tensor) -> torch.Tensor:
r"""
Overview:
Return embedding tensor of the env observation
Arguments:
- x (:obj:`torch.Tensor`): Env raw observation
Returns:
- outputs (:obj:`torch.Tensor`): Embedding tensor
"""
x = self.main(x)
x = self.mid(x)
return x
class TrexModel(nn.Module):
def __init__(self, obs_shape):
super(TrexModel, self).__init__()
if isinstance(obs_shape, int) or len(obs_shape) == 1:
self.encoder = nn.Sequential(FCEncoder(obs_shape, [512, 64]), nn.Linear(64, 1))
# Conv Encoder
elif len(obs_shape) == 3:
self.encoder = TrexConvEncoder(obs_shape)
else:
raise KeyError(
"not support obs_shape for pre-defined encoder: {}, please customize your own Trex model".
format(obs_shape)
)
def cum_return(self, traj: torch.Tensor, mode: str = 'sum') -> Tuple[torch.Tensor, torch.Tensor]:
'''calculate cumulative return of trajectory'''
r = self.encoder(traj)
if mode == 'sum':
sum_rewards = torch.sum(r)
sum_abs_rewards = torch.sum(torch.abs(r))
return sum_rewards, sum_abs_rewards
elif mode == 'batch':
return r, torch.abs(r)
else:
raise KeyError("not support mode: {}, please choose mode=sum or mode=batch".format(mode))
def forward(self, traj_i: torch.Tensor, traj_j: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
'''compute cumulative return for each trajectory and return logits'''
cum_r_i, abs_r_i = self.cum_return(traj_i)
cum_r_j, abs_r_j = self.cum_return(traj_j)
return torch.cat((cum_r_i.unsqueeze(0), cum_r_j.unsqueeze(0)), 0), abs_r_i + abs_r_j
[docs]@REWARD_MODEL_REGISTRY.register('trex')
class TrexRewardModel(BaseRewardModel):
"""
Overview:
The Trex reward model class (https://arxiv.org/pdf/1904.06387.pdf)
Interface:
``estimate``, ``train``, ``load_expert_data``, ``collect_data``, ``clear_date``, \
``__init__``, ``_train``,
Config:
== ==================== ====== ============= ============================================ =============
ID Symbol Type Default Value Description Other(Shape)
== ==================== ====== ============= ============================================ =============
1 ``type`` str trex | Reward model register name, refer |
| to registry ``REWARD_MODEL_REGISTRY`` |
3 | ``learning_rate`` float 0.00001 | learning rate for optimizer |
4 | ``update_per_`` int 100 | Number of updates per collect |
| ``collect`` | |
5 | ``num_trajs`` int 0 | Number of downsampled full trajectories |
6 | ``num_snippets`` int 6000 | Number of short subtrajectories to sample |
== ==================== ====== ============= ============================================ =============
"""
config = dict(
# (str) Reward model register name, refer to registry ``REWARD_MODEL_REGISTRY``.
type='trex',
# (float) The step size of gradient descent.
learning_rate=1e-5,
# (int) How many updates(iterations) to train after collector's one collection.
# Bigger "update_per_collect" means bigger off-policy.
# collect data -> update policy-> collect data -> ...
update_per_collect=100,
# (int) Number of downsampled full trajectories.
num_trajs=0,
# (int) Number of short subtrajectories to sample.
num_snippets=6000,
)
def __init__(self, config: EasyDict, device: str, tb_logger: 'SummaryWriter') -> None: # noqa
"""
Overview:
Initialize ``self.`` See ``help(type(self))`` for accurate signature.
Arguments:
- cfg (:obj:`EasyDict`): Training config
- device (:obj:`str`): Device usage, i.e. "cpu" or "cuda"
- tb_logger (:obj:`SummaryWriter`): Logger, defaultly set as 'SummaryWriter' for model summary
"""
super(TrexRewardModel, self).__init__()
self.cfg = config
assert device in ["cpu", "cuda"] or "cuda" in device
self.device = device
self.tb_logger = tb_logger
self.reward_model = TrexModel(self.cfg.policy.model.obs_shape)
self.reward_model.to(self.device)
self.pre_expert_data = []
self.train_data = []
self.expert_data_loader = None
self.opt = optim.Adam(self.reward_model.parameters(), config.reward_model.learning_rate)
self.train_iter = 0
self.learning_returns = []
self.training_obs = []
self.training_labels = []
self.num_trajs = self.cfg.reward_model.num_trajs
self.num_snippets = self.cfg.reward_model.num_snippets
# minimum number of short subtrajectories to sample
self.min_snippet_length = config.reward_model.min_snippet_length
# maximum number of short subtrajectories to sample
self.max_snippet_length = config.reward_model.max_snippet_length
self.l1_reg = 0
self.data_for_save = {}
self._logger, self._tb_logger = build_logger(
path='./{}/log/{}'.format(self.cfg.exp_name, 'trex_reward_model'), name='trex_reward_model'
)
self.load_expert_data()
def load_expert_data(self) -> None:
"""
Overview:
Getting the expert data.
Effects:
This is a side effect function which updates the expert data attribute \
(i.e. ``self.expert_data``) with ``fn:concat_state_action_pairs``
"""
with open(os.path.join(self.cfg.exp_name, 'episodes_data.pkl'), 'rb') as f:
self.pre_expert_data = pickle.load(f)
with open(os.path.join(self.cfg.exp_name, 'learning_returns.pkl'), 'rb') as f:
self.learning_returns = pickle.load(f)
self.create_training_data()
self._logger.info("num_training_obs: {}".format(len(self.training_obs)))
self._logger.info("num_labels: {}".format(len(self.training_labels)))
def create_training_data(self):
num_trajs = self.num_trajs
num_snippets = self.num_snippets
min_snippet_length = self.min_snippet_length
max_snippet_length = self.max_snippet_length
demo_lengths = []
for i in range(len(self.pre_expert_data)):
demo_lengths.append([len(d) for d in self.pre_expert_data[i]])
self._logger.info("demo_lengths: {}".format(demo_lengths))
max_snippet_length = min(np.min(demo_lengths), max_snippet_length)
self._logger.info("min snippet length: {}".format(min_snippet_length))
self._logger.info("max snippet length: {}".format(max_snippet_length))
# collect training data
max_traj_length = 0
num_bins = len(self.pre_expert_data)
assert num_bins >= 2
# add full trajs (for use on Enduro)
si = np.random.randint(6, size=num_trajs)
sj = np.random.randint(6, size=num_trajs)
step = np.random.randint(3, 7, size=num_trajs)
for n in range(num_trajs):
# pick two random demonstrations
bi, bj = np.random.choice(num_bins, size=(2, ), replace=False)
ti = np.random.choice(len(self.pre_expert_data[bi]))
tj = np.random.choice(len(self.pre_expert_data[bj]))
# create random partial trajs by finding random start frame and random skip frame
traj_i = self.pre_expert_data[bi][ti][si[n]::step[n]] # slice(start,stop,step)
traj_j = self.pre_expert_data[bj][tj][sj[n]::step[n]]
label = int(bi <= bj)
self.training_obs.append((traj_i, traj_j))
self.training_labels.append(label)
max_traj_length = max(max_traj_length, len(traj_i), len(traj_j))
# fixed size snippets with progress prior
rand_length = np.random.randint(min_snippet_length, max_snippet_length, size=num_snippets)
for n in range(num_snippets):
# pick two random demonstrations
bi, bj = np.random.choice(num_bins, size=(2, ), replace=False)
ti = np.random.choice(len(self.pre_expert_data[bi]))
tj = np.random.choice(len(self.pre_expert_data[bj]))
# create random snippets
# find min length of both demos to ensure we can pick a demo no earlier
# than that chosen in worse preferred demo
min_length = min(len(self.pre_expert_data[bi][ti]), len(self.pre_expert_data[bj][tj]))
if bi < bj: # pick tj snippet to be later than ti
ti_start = np.random.randint(min_length - rand_length[n] + 1)
# print(ti_start, len(demonstrations[tj]))
tj_start = np.random.randint(ti_start, len(self.pre_expert_data[bj][tj]) - rand_length[n] + 1)
else: # ti is better so pick later snippet in ti
tj_start = np.random.randint(min_length - rand_length[n] + 1)
# print(tj_start, len(demonstrations[ti]))
ti_start = np.random.randint(tj_start, len(self.pre_expert_data[bi][ti]) - rand_length[n] + 1)
# skip everyother framestack to reduce size
traj_i = self.pre_expert_data[bi][ti][ti_start:ti_start + rand_length[n]:2]
traj_j = self.pre_expert_data[bj][tj][tj_start:tj_start + rand_length[n]:2]
max_traj_length = max(max_traj_length, len(traj_i), len(traj_j))
label = int(bi <= bj)
self.training_obs.append((traj_i, traj_j))
self.training_labels.append(label)
self._logger.info(("maximum traj length: {}".format(max_traj_length)))
return self.training_obs, self.training_labels
def _train(self):
# check if gpu available
device = self.device # torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Assume that we are on a CUDA machine, then this should print a CUDA device:
self._logger.info("device: {}".format(device))
training_inputs, training_outputs = self.training_obs, self.training_labels
loss_criterion = nn.CrossEntropyLoss()
cum_loss = 0.0
training_data = list(zip(training_inputs, training_outputs))
for epoch in range(self.cfg.reward_model.update_per_collect): # todo
np.random.shuffle(training_data)
training_obs, training_labels = zip(*training_data)
for i in range(len(training_labels)):
# traj_i, traj_j has the same length, however, they change as i increases
traj_i, traj_j = training_obs[i] # traj_i is a list of array generated by env.step
traj_i = np.array(traj_i)
traj_j = np.array(traj_j)
traj_i = torch.from_numpy(traj_i).float().to(device)
traj_j = torch.from_numpy(traj_j).float().to(device)
# training_labels[i] is a boolean integer: 0 or 1
labels = torch.tensor([training_labels[i]]).to(device)
# forward + backward + zero out gradient + optimize
outputs, abs_rewards = self.reward_model.forward(traj_i, traj_j)
outputs = outputs.unsqueeze(0)
loss = loss_criterion(outputs, labels) + self.l1_reg * abs_rewards
self.opt.zero_grad()
loss.backward()
self.opt.step()
# print stats to see if learning
item_loss = loss.item()
cum_loss += item_loss
if i % 100 == 99:
self._logger.info("[epoch {}:{}] loss {}".format(epoch, i, cum_loss))
self._logger.info("abs_returns: {}".format(abs_rewards))
cum_loss = 0.0
self._logger.info("check pointing")
if not os.path.exists(os.path.join(self.cfg.exp_name, 'ckpt_reward_model')):
os.makedirs(os.path.join(self.cfg.exp_name, 'ckpt_reward_model'))
torch.save(self.reward_model.state_dict(), os.path.join(self.cfg.exp_name, 'ckpt_reward_model/latest.pth.tar'))
self._logger.info("finished training")
def train(self):
self._train()
# print out predicted cumulative returns and actual returns
sorted_returns = sorted(self.learning_returns, key=lambda s: s[0])
demonstrations = [
x for _, x in sorted(zip(self.learning_returns, self.pre_expert_data), key=lambda pair: pair[0][0])
]
with torch.no_grad():
pred_returns = [self.predict_traj_return(self.reward_model, traj[0]) for traj in demonstrations]
for i, p in enumerate(pred_returns):
self._logger.info("{} {} {}".format(i, p, sorted_returns[i][0]))
info = {
"demo_length": [len(d[0]) for d in self.pre_expert_data],
"min_snippet_length": self.min_snippet_length,
"max_snippet_length": min(np.min([len(d[0]) for d in self.pre_expert_data]), self.max_snippet_length),
"len_num_training_obs": len(self.training_obs),
"lem_num_labels": len(self.training_labels),
"accuracy": self.calc_accuracy(self.reward_model, self.training_obs, self.training_labels),
}
self._logger.info(
"accuracy and comparison:\n{}".format('\n'.join(['{}: {}'.format(k, v) for k, v in info.items()]))
)
def predict_traj_return(self, net, traj):
device = self.device
# torch.set_printoptions(precision=20)
# torch.use_deterministic_algorithms(True)
with torch.no_grad():
rewards_from_obs = net.cum_return(
torch.from_numpy(np.array(traj)).float().to(device), mode='batch'
)[0].squeeze().tolist()
# rewards_from_obs1 = net.cum_return(torch.from_numpy(np.array([traj[0]])).float().to(device))[0].item()
# different precision
return sum(rewards_from_obs) # rewards_from_obs is a list of floats
def calc_accuracy(self, reward_network, training_inputs, training_outputs):
device = self.device
loss_criterion = nn.CrossEntropyLoss()
num_correct = 0.
with torch.no_grad():
for i in range(len(training_inputs)):
label = training_outputs[i]
traj_i, traj_j = training_inputs[i]
traj_i = np.array(traj_i)
traj_j = np.array(traj_j)
traj_i = torch.from_numpy(traj_i).float().to(device)
traj_j = torch.from_numpy(traj_j).float().to(device)
#forward to get logits
outputs, abs_return = reward_network.forward(traj_i, traj_j)
_, pred_label = torch.max(outputs, 0)
if pred_label.item() == label:
num_correct += 1.
return num_correct / len(training_inputs)
def pred_data(self, data):
obs = [default_collate(data[i])['obs'] for i in range(len(data))]
res = [torch.sum(default_collate(data[i])['reward']).item() for i in range(len(data))]
pred_returns = [self.predict_traj_return(self.reward_model, obs[i]) for i in range(len(obs))]
return {'real': res, 'pred': pred_returns}
def estimate(self, data: list) -> List[Dict]:
"""
Overview:
Estimate reward by rewriting the reward key in each row of the data.
Arguments:
- data (:obj:`list`): the list of data used for estimation, with at least \
``obs`` and ``action`` keys.
Effects:
- This is a side effect function which updates the reward values in place.
"""
# NOTE: deepcopy reward part of data is very important,
# otherwise the reward of data in the replay buffer will be incorrectly modified.
train_data_augmented = self.reward_deepcopy(data)
res = collect_states(train_data_augmented)
res = torch.stack(res).to(self.device)
with torch.no_grad():
sum_rewards, sum_abs_rewards = self.reward_model.cum_return(res, mode='batch')
for item, rew in zip(train_data_augmented, sum_rewards): # TODO optimise this loop as well ?
item['reward'] = rew
return train_data_augmented
def collect_data(self, data: list) -> None:
"""
Overview:
Collecting training data formatted by ``fn:concat_state_action_pairs``.
Arguments:
- data (:obj:`Any`): Raw training data (e.g. some form of states, actions, obs, etc)
Effects:
- This is a side effect function which updates the data attribute in ``self``
"""
pass
def clear_data(self) -> None:
"""
Overview:
Clearing training data. \
This is a side effect function which clears the data attribute in ``self``
"""
self.training_obs.clear()
self.training_labels.clear()