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from typing import Dict, List
import math
import random
import pickle
import torch
from ding.utils import REWARD_MODEL_REGISTRY
from .base_reward_model import BaseRewardModel
def collect_state_action_pairs(iterator):
# concat state and action
"""
Overview:
Concate state and action pairs from input iterator.
Arguments:
- iterator (:obj:`Iterable`): Iterables with at least ``obs`` and ``action`` tensor keys.
Returns:
- res (:obj:`Torch.tensor`): State and action pairs.
"""
res = []
for item in iterator:
state = item['obs']
action = item['action']
# s_a = torch.cat([state, action.float()], dim=-1)
res.append((state, action))
return res
@REWARD_MODEL_REGISTRY.register('pwil')
class PwilRewardModel(BaseRewardModel):
"""
Overview:
The Pwil reward model class (https://arxiv.org/pdf/2006.04678.pdf)
Interface:
``estimate``, ``train``, ``load_expert_data``, ``collect_data``, ``clear_date``, \
``__init__``, ``_train``, ``_get_state_distance``, ``_get_action_distance``
Config:
== ================== ===== ============= ======================================= =======================
ID Symbol Type Default Value Description Other(Shape)
== ================== ===== ============= ======================================= =======================
1 ``type`` str pwil | Reward model register name, refer |
| to registry ``REWARD_MODEL_REGISTRY`` |
2 | ``expert_data_`` str expert_data. | Path to the expert dataset | Should be a '.pkl'
| ``path`` .pkl | | file
3 | ``sample_size`` int 1000 | sample data from expert dataset |
| with fixed size |
4 | ``alpha`` int 5 | factor alpha |
5 | ``beta`` int 5 | factor beta |
6 | ``s_size`` int 4 | state size |
7 | ``a_size`` int 2 | action size |
8 | ``clear_buffer`` int 1 | clear buffer per fixed iters | make sure replay
``_per_iters`` | buffer's data count
| isn't too few.
| (code work in entry)
== ================== ===== ============= ======================================= =======================
Properties:
- reward_table (:obj: `Dict`): In this algorithm, reward model is a dictionary.
"""
config = dict(
# (str) Reward model register name, refer to registry ``REWARD_MODEL_REGISTRY``.
type='pwil',
# (str) Path to the expert dataset.
# expert_data_path='expert_data.pkl',
# (int) Sample data from expert dataset with fixed size.
sample_size=1000,
# r = alpha * exp((-beta*T/sqrt(|s_size|+ |a_size|))*c_i)
# key idea for this reward is to minimize.
# the Wasserstein distance between the state-action distribution.
# (int) Factor alpha.
alpha=5,
# (int) Factor beta.
beta=5,
#（int）State size.
# s_size=4,
# (int) Action size.
# a_size=2,
# (int) Clear buffer per fixed iters.
clear_buffer_per_iters=1,
)
def __init__(self, config: Dict, device: str, tb_logger: 'SummaryWriter') -> None: # noqa
"""
Overview:
Initialize ``self.`` See ``help(type(self))`` for accurate signature.
Arguments:
- cfg (:obj:`Dict`): Training config
- device (:obj:`str`): Device usage, i.e. "cpu" or "cuda"
- tb_logger (:obj:`str`): Logger, defaultly set as 'SummaryWriter' for model summary
"""
super(PwilRewardModel, self).__init__()
self.cfg: Dict = config
assert device in ["cpu", "cuda"] or "cuda" in device
self.device = device
self.expert_data: List[tuple] = []
self.train_data: List[tuple] = []
# In this algo, model is a dict
self.reward_table: Dict = {}
self.T: int = 0
self.load_expert_data()
def load_expert_data(self) -> None:
"""
Overview:
Getting the expert data from ``config['expert_data_path']`` attribute in self
Effects:
This is a side effect function which updates the expert data attribute (e.g. ``self.expert_data``); \
in this algorithm, also the ``self.expert_s``, ``self.expert_a`` for states and actions are updated.
"""
with open(self.cfg.expert_data_path, 'rb') as f:
self.expert_data = pickle.load(f)
print("the data size is:", len(self.expert_data))
sample_size = min(self.cfg.sample_size, len(self.expert_data))
self.expert_data = random.sample(self.expert_data, sample_size)
self.expert_data = [(item['obs'], item['action']) for item in self.expert_data]
self.expert_s, self.expert_a = list(zip(*self.expert_data))
print('the expert data demonstrations is:', len(self.expert_data))
def collect_data(self, data: list) -> None:
"""
Overview:
Collecting training data formatted by ``fn:concat_state_action_pairs``.
Arguments:
- data (:obj:`list`): 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``; \
in this algorithm, also the ``s_size``, ``a_size`` for states and actions are updated in the \
attribute in ``self.cfg`` Dict; ``reward_factor`` also updated as ``collect_data`` called.
"""
self.train_data.extend(collect_state_action_pairs(data))
self.T = len(self.train_data)
s_size = self.cfg.s_size
a_size = self.cfg.a_size
beta = self.cfg.beta
self.reward_factor = -beta * self.T / math.sqrt(s_size + a_size)
def train(self) -> None:
"""
Overview:
Training the Pwil reward model.
"""
self._train(self.train_data)
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_table`` with ``(obs,action)`` \
tuples from input.
"""
# 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)
for item in train_data_augmented:
s = item['obs']
a = item['action']
if (s, a) in self.reward_table:
item['reward'] = self.reward_table[(s, a)]
else:
# when (s, a) pair is not trained, set the reward value to default value(e.g.: 0)
item['reward'] = torch.zeros_like(item['reward'])
return train_data_augmented
def _get_state_distance(self, s1: list, s2: list) -> torch.Tensor:
"""
Overview:
Getting distances of states given 2 state lists. One single state \
is of shape ``torch.Size([n])`` (``n`` referred in in-code comments)
Arguments:
- s1 (:obj:`torch.Tensor list`): the 1st states' list of size M
- s2 (:obj:`torch.Tensor list`): the 2nd states' list of size N
Returns:
- distance (:obj:`torch.Tensor`) Euclidean distance tensor of \
the state tensor lists, of size M x N.
"""
# Format the values in the tensors to be of float type
s1 = torch.stack(s1).float()
s2 = torch.stack(s2).float()
M, N = s1.shape[0], s2.shape[0]
# Automatically fill in length
s1 = s1.view(M, -1)
s2 = s2.view(N, -1)
# Automatically fill in & format the tensor size to be (MxNxn)
s1 = s1.unsqueeze(1).repeat(1, N, 1)
s2 = s2.unsqueeze(0).repeat(M, 1, 1)
# Return the distance tensor of size MxN
return ((s1 - s2) ** 2).mean(dim=-1)
def _get_action_distance(self, a1: list, a2: list) -> torch.Tensor:
# TODO the metric of action distance maybe different from envs
"""
Overview:
Getting distances of actions given 2 action lists. One single action \
is of shape ``torch.Size([n])`` (``n`` referred in in-code comments)
Arguments:
- a1 (:obj:`torch.Tensor list`): the 1st actions' list of size M
- a2 (:obj:`torch.Tensor list`): the 2nd actions' list of size N
Returns:
- distance (:obj:`torch.Tensor`) Euclidean distance tensor of \
the action tensor lists, of size M x N.
"""
a1 = torch.stack(a1).float()
a2 = torch.stack(a2).float()
M, N = a1.shape[0], a2.shape[0]
a1 = a1.view(M, -1)
a2 = a2.view(N, -1)
a1 = a1.unsqueeze(1).repeat(1, N, 1)
a2 = a2.unsqueeze(0).repeat(M, 1, 1)
return ((a1 - a2) ** 2).mean(dim=-1)
def _train(self, data: list):
"""
Overview:
Helper function for ``train``, find the min disctance ``s_e``, ``a_e``.
Arguments:
- data (:obj:`list`): Raw training data (e.g. some form of states, actions, obs, etc)
Effects:
- This is a side effect function which updates the ``reward_table`` attribute in ``self`` .
"""
batch_s, batch_a = list(zip(*data))
s_distance_matrix = self._get_state_distance(batch_s, self.expert_s)
a_distance_matrix = self._get_action_distance(batch_a, self.expert_a)
distance_matrix = s_distance_matrix + a_distance_matrix
w_e_list = [1 / len(self.expert_data)] * len(self.expert_data)
for i, item in enumerate(data):
s, a = item
w_pi = 1 / self.T
c = 0
expert_data_idx = torch.arange(len(self.expert_data)).tolist()
while w_pi > 0:
selected_dist = distance_matrix[i, expert_data_idx]
nearest_distance = selected_dist.min().item()
nearest_index_selected = selected_dist.argmin().item()
nearest_index = expert_data_idx[nearest_index_selected]
if w_pi >= w_e_list[nearest_index]:
c = c + nearest_distance * w_e_list[nearest_index]
w_pi = w_pi - w_e_list[nearest_index]
expert_data_idx.pop(nearest_index_selected)
else:
c = c + w_pi * nearest_distance
w_e_list[nearest_index] = w_e_list[nearest_index] - w_pi
w_pi = 0
reward = self.cfg.alpha * math.exp(self.reward_factor * c)
self.reward_table[(s, a)] = torch.FloatTensor([reward])
def clear_data(self) -> None:
"""
Overview:
Clearing training data. \
This is a side effect function which clears the data attribute in ``self``
"""
self.train_data.clear()
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