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group_robust_dpo.py
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import copy
import cvxpy as cp
import numpy as np
from typing import List,Set, Union
from envs.group_linear_bandit import GroupLinearBandit
from utils.collect_data import GroupTransition, ret_uniform_policy, collect_preference_data
from utils.utils import softmax, sigmoid
from utils.logger import Logger
import random
import wandb
from collections import defaultdict
class GroupRobustDirectPolicyOptimization:
def __init__(
self,
state_dim: int,
action_num: int,
group_num: int,
feature_dim: int,
feature_func,
ref_policy,
reg_coef: float,
step_size: float,
num_iters: int,
exp_step_size: float,
batch_size: int,
is_adaptive: bool = False,
ada_coef: float = None,
logger: Logger = None,
wandb_use: bool = False,
weighted_batches: bool = False,
adj: str = None,
importance_sampling: bool = False,
importance_sampling_weights: str = 'None',
ipo_grad_type: str = 'justdpo',
param_limit: int = 1,
use_closed_form: bool=False,
lamba: float =0,
train_agent: bool=True
) -> None:
self.state_dim = state_dim
self.action_num = action_num
self.feature_dim = feature_dim
self.group_num = group_num
self.feature_func = feature_func
self.step_size = step_size
self.num_iters = num_iters
self.ref_policy = ref_policy
self.reg_coef = reg_coef
self.logger = logger
self.wandb_use=wandb_use
self.ipo_grad_type=ipo_grad_type
self.group_weights=np.ones(group_num)/group_num
self.exp_step_size=exp_step_size
self.batch_size=batch_size
self.is_adaptive = is_adaptive
self.ada_coef = ada_coef
self.hist_grad_squared_norm = 0.0
self.hist_group_loss=np.zeros(group_num)
# initialize the policy parameter
self.param = np.random.uniform(0, param_limit, self.feature_dim)
self.weighted_batches=weighted_batches
self.importance_sampling=importance_sampling
if importance_sampling_weights=='None':
self.importance_sampling_weights=None
else:
self.importance_sampling_weights=importance_sampling_weights
if adj is not None:
# process generalization adjustment stuff
adjustments = [float(c) for c in adj.split(',')]
assert len(adjustments) in (1, self.group_num)
if len(adjustments)==1:
adjustments = adjustments[0]*np.ones(self.group_num)
else:
adjustments = np.array(adjustments)
self.adj = adjustments
else:
self.adj = np.zeros(self.group_num)
self.use_closed_form=use_closed_form
self.lamba=lamba
self.train_agent=train_agent
print(self.step_size,weighted_batches,self.adj)
def ret_action_prob(self, state: np.ndarray, group_id: int) -> np.ndarray:
arr = np.zeros(self.action_num, np.float32)
for action_idx in range(self.action_num):
feature = self.feature_func(state, action_idx, group_id)
arr[action_idx] = np.dot(feature, self.param)
prob = softmax(arr)
return prob
def ret_policy(self):
action_num = self.action_num
feature_func = copy.deepcopy(self.feature_func)
param = self.param
def policy(state: np.ndarray, group_id: int) -> np.ndarray:
arr = np.zeros(action_num, np.float32)
for action_idx in range(action_num):
feature = feature_func(state, action_idx, group_id)
arr[action_idx] = np.dot(feature, param)
prob = softmax(arr)
return prob
return policy
def sample_action(self, state: np.ndarray, group_id: int) -> int:
prob = self.action_prob(state, group_id)
sampled_act = np.random.choice(a=self.action_num, size=1, replace=True, p=prob)
return sampled_act
def update_once(self, dataset: List[GroupTransition]) -> float:
grad = np.zeros_like(self.param)
for transition in dataset:
state, action_one, action_two, group_id, pref = (
transition.state,
transition.action_0,
transition.action_1,
transition.group_id,
transition.pref,
)
pref_act = action_two if pref == 1 else action_one
non_pref_act = action_two if pref == 0 else action_one
feat_pref_act, feat_non_pref_act = (
self.feature_func(state, pref_act, group_id),
self.feature_func(state, non_pref_act,group_id),
)
#print(feat_pref_act,feat_non_pref_act,self.param)
cur_policy_act_prob = self.ret_action_prob(state,group_id)
ref_policy_act_prob = self.ref_policy(state,group_id)
log_ratio_diff = self.reg_coef * (
np.log(cur_policy_act_prob[pref_act] + 1e-6)
- np.log(ref_policy_act_prob[pref_act] + 1e-6)
- np.log(cur_policy_act_prob[non_pref_act] + 1e-6)
+ np.log(ref_policy_act_prob[non_pref_act] + 1e-6)
)
coef = sigmoid(-log_ratio_diff)
neg_cur_data_grad = (
self.reg_coef * coef * (feat_pref_act - feat_non_pref_act)
)
grad -= neg_cur_data_grad
grad /= len(dataset)
self.hist_grad_squared_norm += np.sum(np.square(grad))
if self.is_adaptive:
step_size = self.ada_coef / np.sqrt(self.hist_grad_squared_norm)
else:
step_size = self.step_size
self.param = self.param - step_size * grad
return np.sqrt(np.sum(np.square(grad)))
def batch_update_once(self, dataset: List[GroupTransition],batch_size: int, unique_group_ids: Set[int]) -> float:
def sample_group_transition(group_id):
#print(group_id)
#print(dataset[0].group_id)
group_transitions_with_id = [transition for transition in dataset if transition.group_id == group_id]
return random.choice(group_transitions_with_id)
grad = np.zeros_like(self.param)
if self.weighted_batches==True:
if len(unique_group_ids)==self.group_num:
group_id_mat=np.floor(np.random.uniform(
0, self.group_num, size=(batch_size)
)).astype(int)#sample group_ids according to batchsize
#print(group_id_mat)
else:
group_id_mat = np.array(random.choices(list(unique_group_ids), k=batch_size)).astype(int)
#print(group_id_mat)
# Sample GroupTransitions for each group_id in the array
sampled_group_transitions = [sample_group_transition(group_id) for group_id in group_id_mat]#within that group choose a transition
else:
if batch_size<len(dataset):
sampled_group_transitions=random.choices(dataset,k=batch_size)
else:
sampled_group_transitions=dataset
#print(len(sampled_group_transitions),'non_weighted')
# Display the sampled GroupTransitions
#for group_id, sampled_transition in zip(group_id_mat, sampled_group_transitions):
# print(f"Group ID: {group_id}, Sampled Transition: {sampled_transition}")
group_loss=np.zeros(self.group_num)
cur_group_counts=np.zeros(self.group_num)
for transition in sampled_group_transitions:
state, action_one, action_two, group_id, pref = (
transition.state,
transition.action_0,
transition.action_1,
transition.group_id,
transition.pref,
)
pref_act = action_two if pref == 1 else action_one
non_pref_act = action_two if pref == 0 else action_one
feat_pref_act, feat_non_pref_act = (
self.feature_func(state, pref_act, group_id),
self.feature_func(state, non_pref_act,group_id),
)
#print(feat_pref_act,feat_non_pref_act,self.param)
cur_policy_act_prob = self.ret_action_prob(state,group_id)
ref_policy_act_prob = self.ref_policy(state,group_id)
log_ratio_diff = self.reg_coef * (
np.log(cur_policy_act_prob[pref_act] + 1e-6)
- np.log(ref_policy_act_prob[pref_act] + 1e-6)
- np.log(cur_policy_act_prob[non_pref_act] + 1e-6)
+ np.log(ref_policy_act_prob[non_pref_act] + 1e-6)
)
if self.ipo_grad_type=='linear':
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
coef=-2*lin_diff/self.reg_coef
elif self.ipo_grad_type=='log':
log_diff=(
np.log((cur_policy_act_prob[pref_act]*ref_policy_act_prob[non_pref_act])/(cur_policy_act_prob[non_pref_act]*ref_policy_act_prob[pref_act])+1e-6 )
)
coef=-2*(log_diff-0.5*(1/self.reg_coef))/self.reg_coef
elif self.ipo_grad_type=='justdpo':
coef = sigmoid(-log_ratio_diff)
else:
raise ValueError('value not implemented')
neg_cur_data_grad = (
self.reg_coef * coef * (feat_pref_act - feat_non_pref_act)
)
#print(self.group_weights)
#print(group_id,self.group_weights[group_id])
grad -= 2*self.group_weights[group_id]*neg_cur_data_grad#weighted gradient calculation
#grad -= neg_cur_data_grad
#if self.adj==None:
# group_loss[group_id]+=-np.log(sigmoid(log_ratio_diff))#calculate group losses
#else:
if self.ipo_grad_type=='linear':
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
group_loss[group_id]+=np.square(lin_diff)+self.adj[group_id]/np.sqrt(self.group_counts[group_id])
elif self.ipo_grad_type=='log':
#print('log')
log_diff=(
np.log((cur_policy_act_prob[pref_act]*ref_policy_act_prob[non_pref_act])/(cur_policy_act_prob[non_pref_act]*ref_policy_act_prob[pref_act])+1e-6 )
)
group_loss[group_id]+=np.square((log_diff-0.5*(1/self.reg_coef)))+self.adj[group_id]/np.sqrt(self.group_counts[group_id])
elif self.ipo_grad_type=='justdpo':
group_loss[group_id]+=-np.log(sigmoid(log_ratio_diff))+self.adj[group_id]/np.sqrt(self.group_counts[group_id]) #calculate group losses
else:
raise ValueError('value not implemented')
#print(group_id,self.adj[group_id]/np.sqrt(self.group_counts[group_id]) )
cur_group_counts[group_id]+=1
grad /= len(sampled_group_transitions)
group_loss=group_loss/cur_group_counts
#print(group_loss)
if self.importance_sampling==False:
#print(self.group_weights,group_loss,np.exp(self.exp_step_size*group_loss))
self.group_weights=self.group_weights*np.exp(self.exp_step_size*group_loss)#update weights based on group loss calculated
#print(self.group_weights)
self.group_weights=self.group_weights/np.sum(self.group_weights)#normalize the weights
self.hist_grad_squared_norm += np.sum(np.square(grad))
self.hist_group_loss+=group_loss
self.group_loss=group_loss
if self.is_adaptive:
step_size = self.ada_coef / np.sqrt(self.hist_grad_squared_norm)
else:
step_size = self.step_size
#print(grad)
self.param = self.param - step_size * (grad)
#self.param=np.array([1.0,2.0])
live_grad=self.WeightedRegression(sampled_group_transitions,self.lamba)
return np.sqrt(np.sum(np.square(grad))), live_grad
def batch_update_closed_form(self, dataset: List[GroupTransition],batch_size: int, unique_group_ids: Set[int]) -> float:
def sample_group_transition(group_id):
#print(group_id)
#print(dataset[0].group_id)
group_transitions_with_id = [transition for transition in dataset if transition.group_id == group_id]
return random.choice(group_transitions_with_id)
grad = np.zeros_like(self.param)
if self.weighted_batches==True:
if len(unique_group_ids)==self.group_num:
group_id_mat=np.floor(np.random.uniform(
0, self.group_num, size=(batch_size)
)).astype(int)#sample group_ids according to batchsize
#print(group_id_mat)
else:
group_id_mat = np.array(random.choices(list(unique_group_ids), k=batch_size)).astype(int)
#print(group_id_mat)
# Sample GroupTransitions for each group_id in the array
sampled_group_transitions = [sample_group_transition(group_id) for group_id in group_id_mat]#within that group choose a transition
else:
if batch_size<len(dataset):
sampled_group_transitions=random.choices(dataset,k=batch_size)
else:
sampled_group_transitions=dataset
#print(len(sampled_group_transitions),'non_weighted')
# Display the sampled GroupTransitions
#for group_id, sampled_transition in zip(group_id_mat, sampled_group_transitions):
# print(f"Group ID: {group_id}, Sampled Transition: {sampled_transition}")
group_loss=np.zeros(self.group_num)
cur_group_counts=np.zeros(self.group_num)
for transition in sampled_group_transitions:
state, action_one, action_two, group_id, pref = (
transition.state,
transition.action_0,
transition.action_1,
transition.group_id,
transition.pref,
)
pref_act = action_two if pref == 1 else action_one
non_pref_act = action_two if pref == 0 else action_one
feat_pref_act, feat_non_pref_act = (
self.feature_func(state, pref_act, group_id),
self.feature_func(state, non_pref_act,group_id),
)
#print(feat_pref_act,feat_non_pref_act,self.param)
cur_policy_act_prob = self.ret_action_prob(state,group_id)
ref_policy_act_prob = self.ref_policy(state,group_id)
log_ratio_diff = self.reg_coef * (
np.log(cur_policy_act_prob[pref_act] + 1e-6)
- np.log(ref_policy_act_prob[pref_act] + 1e-6)
- np.log(cur_policy_act_prob[non_pref_act] + 1e-6)
+ np.log(ref_policy_act_prob[non_pref_act] + 1e-6)
)
if self.ipo_grad_type=='linear':
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
coef=-2*lin_diff/self.reg_coef
elif self.ipo_grad_type=='log':
log_diff=(
np.log((cur_policy_act_prob[pref_act]*ref_policy_act_prob[non_pref_act])/(cur_policy_act_prob[non_pref_act]*ref_policy_act_prob[pref_act])+1e-6 )
)
coef=-2*(log_diff-0.5*(1/self.reg_coef))/self.reg_coef
elif self.ipo_grad_type=='justdpo':
coef = sigmoid(-log_ratio_diff)
else:
raise ValueError('value not implemented')
neg_cur_data_grad = (
self.reg_coef * coef * (feat_pref_act - feat_non_pref_act)
)
#print(self.group_weights)
#print(group_id,self.group_weights[group_id])
grad -= 2*self.group_weights[group_id]*neg_cur_data_grad
if self.ipo_grad_type=='linear':
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
group_loss[group_id]+=np.square(lin_diff)+self.adj[group_id]/np.sqrt(self.group_counts[group_id])
elif self.ipo_grad_type=='log':
#print('log')
log_diff=(
np.log((cur_policy_act_prob[pref_act]*ref_policy_act_prob[non_pref_act])/(cur_policy_act_prob[non_pref_act]*ref_policy_act_prob[pref_act])+1e-6 )
)
group_loss[group_id]+=np.square((log_diff-0.5*(1/self.reg_coef)))+self.adj[group_id]/np.sqrt(self.group_counts[group_id])
elif self.ipo_grad_type=='justdpo':
group_loss[group_id]+=-np.log(sigmoid(log_ratio_diff))+self.adj[group_id]/np.sqrt(self.group_counts[group_id]) #calculate group losses
else:
raise ValueError('value not implemented')
#print(group_id,self.adj[group_id]/np.sqrt(self.group_counts[group_id]) )
cur_group_counts[group_id]+=1
grad /= len(sampled_group_transitions)
group_loss=group_loss/cur_group_counts
#print(group_loss)
if self.importance_sampling==False:
#print(self.group_weights,group_loss,np.exp(self.exp_step_size*group_loss))
self.group_weights=self.group_weights*np.exp(self.exp_step_size*group_loss)#update weights based on group loss calculated
#print(self.group_weights)
self.group_weights=self.group_weights/np.sum(self.group_weights)#normalize the weights
self.hist_grad_squared_norm += np.sum(np.square(grad))
self.hist_group_loss+=group_loss
self.group_loss=group_loss
if self.is_adaptive:
step_size = self.ada_coef / np.sqrt(self.hist_grad_squared_norm)
else:
step_size = self.step_size
#print(grad)
live_grad=self.WeightedRegression(sampled_group_transitions,self.lamba)
#self.param=np.array([1.0,2.0])
return np.sqrt(np.sum(np.square(grad))),live_grad
def WeightedRegression(self, dataset: List[GroupTransition], lamba: float)-> float:
Y=[]
w=[]
for transition in dataset:
state, action_one, action_two, group_id, pref = (
transition.state,
transition.action_0,
transition.action_1,
transition.group_id,
transition.pref,
)
pref_act = action_two if pref == 1 else action_one
non_pref_act = action_two if pref == 0 else action_one
feat_pref_act, feat_non_pref_act = (
self.feature_func(state, pref_act, group_id),
self.feature_func(state, non_pref_act,group_id),
)
Y.append(feat_pref_act-feat_non_pref_act)
w.append(self.group_weights[group_id])
Y=np.array(Y)
w=np.array(w)
#print(w)
#print(Y.shape,np.diag(w).shape,([email protected]).T.shape,(([email protected]).T-1/(2*self.reg_coef)).dot(Y).shape)
coef=np.linalg.inv(Y.transpose()@np.diag(w)@Y+lamba*np.eye(Y.shape[1]))
#print(np.linalg.det(np.matmul(Y.transpose(),Y)))
variate=np.matmul(np.matmul(Y.transpose(),np.diag(w)),np.ones([len(dataset),1]))
self.param=np.matmul(coef,variate).ravel()/(2*self.reg_coef)
live_grad=(np.diag(w).dot(([email protected]).T-1/(2*self.reg_coef))).dot(Y)+lamba*self.param
return np.sqrt(np.sum(np.square(live_grad)))
def evaluate_ipo_loss(self, dataset: List[GroupTransition], policy=None) -> float:
"""
Evaluate the loss on the dataset for any policy.
"""
if policy is None:
policy = self.ret_policy()
loss = 0.0
for transition in dataset:
state, action_one, action_two, group_id, pref = (
transition.state,
transition.action_0,
transition.action_1,
transition.group_id,
transition.pref,
)
pref_act = action_two if pref == 1 else action_one
non_pref_act = action_two if pref == 0 else action_one
feat_pref_act, feat_non_pref_act = (
self.feature_func(state, pref_act, group_id),
self.feature_func(state, non_pref_act,group_id),
)
eval_policy_act_prob = policy(state,group_id)
ref_policy_act_prob = self.ref_policy(state,group_id)
# if np.isclose(eval_policy_act_prob[pref_act], 0.) or np.isclose(eval_policy_act_prob[non_pref_act], 0.):
# print(eval_policy_act_prob[pref_act], eval_policy_act_prob[non_pref_act])
if self.ipo_grad_type=='linear':
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
coef=lin_diff
elif self.ipo_grad_type=='log':
log_diff=(
np.log((eval_policy_act_prob[pref_act]*ref_policy_act_prob[non_pref_act])/(eval_policy_act_prob[non_pref_act]*ref_policy_act_prob[pref_act]) + 1e-6)
)
coef=(log_diff-0.5*(1/self.reg_coef))
else:
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
coef=lin_diff
loss = loss+ np.square(coef)
loss /= len(dataset)
return loss
def evaluate_weighted_ipo_loss(self, dataset: List[GroupTransition], policy=None) -> float:
"""
Evaluate the loss on the dataset for any policy.
"""
if policy is None:
policy = self.ret_policy()
loss = 0.0
for transition in dataset:
state, action_one, action_two, group_id, pref = (
transition.state,
transition.action_0,
transition.action_1,
transition.group_id,
transition.pref,
)
pref_act = action_two if pref == 1 else action_one
non_pref_act = action_two if pref == 0 else action_one
feat_pref_act, feat_non_pref_act = (
self.feature_func(state, pref_act, group_id),
self.feature_func(state, non_pref_act,group_id),
)
eval_policy_act_prob = policy(state,group_id)
ref_policy_act_prob = self.ref_policy(state,group_id)
if self.ipo_grad_type=='linear':
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
coef=lin_diff
elif self.ipo_grad_type=='log':
log_diff=(
np.log((eval_policy_act_prob[pref_act]*ref_policy_act_prob[non_pref_act])/(eval_policy_act_prob[non_pref_act]*ref_policy_act_prob[pref_act]) + 1e-6)
)
coef=(log_diff-0.5*(1/self.reg_coef))
else:
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
coef=lin_diff
loss += self.group_weights[group_id]*np.square(coef)+self.adj[group_id]/np.sqrt(self.group_counts[group_id])
loss /= len(dataset)
loss=loss*self.group_num###for correct comparison as unweighted train loss should multiply 1/num_groups to all
return loss
def evaluate_ipo_grp_loss(self, dataset: List[GroupTransition], policy=None) -> float:
"""
Evaluate the loss on the dataset for any policy.
"""
if policy is None:
policy = self.ret_policy()
loss = np.zeros(self.group_num)
counts = np.zeros(self.group_num)
for transition in dataset:
state, action_one, action_two, group_id, pref = (
transition.state,
transition.action_0,
transition.action_1,
transition.group_id,
transition.pref,
)
pref_act = action_two if pref == 1 else action_one
non_pref_act = action_two if pref == 0 else action_one
feat_pref_act, feat_non_pref_act = (
self.feature_func(state, pref_act, group_id),
self.feature_func(state, non_pref_act,group_id),
)
eval_policy_act_prob = policy(state,group_id)
ref_policy_act_prob = self.ref_policy(state,group_id)
if self.ipo_grad_type=='linear':
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
coef=lin_diff
elif self.ipo_grad_type=='log':
log_diff=(
np.log((eval_policy_act_prob[pref_act]*ref_policy_act_prob[non_pref_act])/(eval_policy_act_prob[non_pref_act]*ref_policy_act_prob[pref_act]) + 1e-6)
)
coef=(log_diff-0.5*(1/self.reg_coef))
else:
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
coef=lin_diff
loss[group_id] += np.square(coef) +self.adj[group_id]/np.sqrt(self.group_counts[group_id])
#loss[group_id] -= np.log(sigmoid(log_ratio_diff))
counts[group_id]+=1
loss = loss/counts
return loss
def evaluate_ipo_grad(self, dataset: List[GroupTransition], policy=None) -> float:
"""
Evaluate the loss on the dataset for any policy.
"""
if policy is None:
policy = self.ret_policy()
grad = np.zeros_like(self.param)
for transition in dataset:
state, action_one, action_two, group_id, pref = (
transition.state,
transition.action_0,
transition.action_1,
transition.group_id,
transition.pref,
)
pref_act = action_two if pref == 1 else action_one
non_pref_act = action_two if pref == 0 else action_one
feat_pref_act, feat_non_pref_act = (
self.feature_func(state, pref_act, group_id),
self.feature_func(state, non_pref_act,group_id),
)
eval_policy_act_prob = policy(state,group_id)
ref_policy_act_prob = self.ref_policy(state,group_id)
# if np.isclose(eval_policy_act_prob[pref_act], 0.) or np.isclose(eval_policy_act_prob[non_pref_act], 0.):
# print(eval_policy_act_prob[pref_act], eval_policy_act_prob[non_pref_act])
if self.ipo_grad_type=='linear':
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
coef=lin_diff
elif self.ipo_grad_type=='log':
log_diff=(
np.log((eval_policy_act_prob[pref_act]*ref_policy_act_prob[non_pref_act])/(eval_policy_act_prob[non_pref_act]*ref_policy_act_prob[pref_act]) + 1e-6)
)
coef=(log_diff-0.5*(1/self.reg_coef))
else:
print(self.param,feat_pref_act-feat_non_pref_act)
lin_diff=(feat_pref_act-feat_non_pref_act)@(self.param)-0.5*(1/self.reg_coef)
coef=lin_diff
cur_data_grad = (
2* coef * (feat_pref_act - feat_non_pref_act)
)
grad += cur_data_grad
grad /= len(dataset)
return np.sqrt(np.sum(np.square(grad)))
def evaluate_loss(self, dataset: List[GroupTransition], policy=None) -> float:
"""
Evaluate the loss on the dataset for any policy.
"""
if policy is None:
policy = self.ret_policy()
loss = 0.0
for transition in dataset:
state, action_one, action_two, group_id, pref = (
transition.state,
transition.action_0,
transition.action_1,
transition.group_id,
transition.pref,
)
pref_act = action_two if pref == 1 else action_one
non_pref_act = action_two if pref == 0 else action_one
eval_policy_act_prob = policy(state,group_id)
ref_policy_act_prob = self.ref_policy(state,group_id)
# if np.isclose(eval_policy_act_prob[pref_act], 0.) or np.isclose(eval_policy_act_prob[non_pref_act], 0.):
# print(eval_policy_act_prob[pref_act], eval_policy_act_prob[non_pref_act])
log_ratio_diff = self.reg_coef * (
np.log(eval_policy_act_prob[pref_act] + 1e-6)
- np.log(ref_policy_act_prob[pref_act] + 1e-6)
- np.log(eval_policy_act_prob[non_pref_act] + 1e-6)
+ np.log(ref_policy_act_prob[non_pref_act] + 1e-6)
)
loss -= np.log(sigmoid(log_ratio_diff))
loss /= len(dataset)
return loss
def evaluate_weighted_loss(self, dataset: List[GroupTransition], policy=None) -> float:
"""
Evaluate the loss on the dataset for any policy.
"""
if policy is None:
policy = self.ret_policy()
loss = 0.0
for transition in dataset:
state, action_one, action_two, group_id, pref = (
transition.state,
transition.action_0,
transition.action_1,
transition.group_id,
transition.pref,
)
pref_act = action_two if pref == 1 else action_one
non_pref_act = action_two if pref == 0 else action_one
eval_policy_act_prob = policy(state,group_id)
ref_policy_act_prob = self.ref_policy(state,group_id)
# if np.isclose(eval_policy_act_prob[pref_act], 0.) or np.isclose(eval_policy_act_prob[non_pref_act], 0.):
# print(eval_policy_act_prob[pref_act], eval_policy_act_prob[non_pref_act])
log_ratio_diff = self.reg_coef * (
np.log(eval_policy_act_prob[pref_act] + 1e-6)
- np.log(ref_policy_act_prob[pref_act] + 1e-6)
- np.log(eval_policy_act_prob[non_pref_act] + 1e-6)
+ np.log(ref_policy_act_prob[non_pref_act] + 1e-6)
)
loss += -self.group_weights[group_id]*np.log(sigmoid(log_ratio_diff))+self.adj[group_id]/np.sqrt(self.group_counts[group_id])
loss /= len(dataset)
loss=loss*self.group_num###for correct comparison as unweighted train loss should multiply 1/num_groups to all
return loss
def evaluate_grp_loss(self, dataset: List[GroupTransition], policy=None) -> float:
"""
Evaluate the loss on the dataset for any policy.
"""
if policy is None:
policy = self.ret_policy()
loss = np.zeros(self.group_num)
counts = np.zeros(self.group_num)
for transition in dataset:
state, action_one, action_two, group_id, pref = (
transition.state,
transition.action_0,
transition.action_1,
transition.group_id,
transition.pref,
)
pref_act = action_two if pref == 1 else action_one
non_pref_act = action_two if pref == 0 else action_one
eval_policy_act_prob = policy(state,group_id)
ref_policy_act_prob = self.ref_policy(state,group_id)
# if np.isclose(eval_policy_act_prob[pref_act], 0.) or np.isclose(eval_policy_act_prob[non_pref_act], 0.):
# print(eval_policy_act_prob[pref_act], eval_policy_act_prob[non_pref_act])
log_ratio_diff = self.reg_coef * (
np.log(eval_policy_act_prob[pref_act] + 1e-6)
- np.log(ref_policy_act_prob[pref_act] + 1e-6)
- np.log(eval_policy_act_prob[non_pref_act] + 1e-6)
+ np.log(ref_policy_act_prob[non_pref_act] + 1e-6)
)
loss[group_id] -= np.log(sigmoid(log_ratio_diff))
counts[group_id]+=1
loss = loss/counts
return loss
def random_train(self, dataset: List[GroupTransition],
val_dataset: list[GroupTransition],
test_dataset: list[GroupTransition], env: GroupLinearBandit, optimal_reward: List[float]) -> float:
grad_norm=self.evaluate_ipo_grad(dataset)
live_grad=grad_norm
train_loss=self.evaluate_ipo_loss(dataset)
val_loss = self.evaluate_ipo_loss(val_dataset)
train_grp_loss = self.evaluate_ipo_grp_loss(dataset)
val_grp_loss = self.evaluate_ipo_grp_loss(val_dataset)
kl_dist=self.evaluate_KL(env=env,states=test_dataset)
formatted_kl=", ".join([f"{kld:.4f}" for kld in kl_dist])
#Evaluate the reward on the test dataset:
#print(optimal_reward,self.evaluate_reward(env=env,
# states=test_dataset))
rew_err = [float(a - b)/a for a, b in zip(optimal_reward,self.evaluate_reward(env=env,
states=test_dataset) )]
formatted_rew=", ".join([f"{reward:.4f}" for reward in rew_err])
max_reward_err=max(rew_err)
max_reward_err_index=rew_err.index(max_reward_err)
max_kl_dist=max(kl_dist)
max_kl_dist_index=kl_dist.index(max_kl_dist)
max_train_grp_loss=np.max(train_grp_loss)
max_val_grp_loss=np.max(val_grp_loss)
max_train_grp_loss_index=np.argmax(train_grp_loss)
max_val_grp_loss_index=np.argmax(val_grp_loss)
step=0
logging_str = (f"Iteration: {step: d}, train_loss: {train_loss: .4f}, "
f"val_loss: {val_loss: .4f}, grad_norm: {grad_norm:.4f}, live_grad: {live_grad:.4f}, "
f"reward_err: {formatted_rew}, KL_dist: {formatted_kl}, param: {self.param}, "
f"train_grp_loss: {train_grp_loss}, val_grp_loss: {val_grp_loss}, "
f"max_reward_err: {max_reward_err: .4f}, max_reward_err_index: {max_reward_err_index}, "
f"max_kl_dist: {max_kl_dist: .4f}, max_kl_dist_index: {max_kl_dist_index}, "
f"max_train_grp_loss: {max_train_grp_loss: .4f}, max_train_grp_loss_index: {max_train_grp_loss_index}, "
f"max_val_grp_loss: {max_val_grp_loss: .4f}, max_val_grp_loss_index: {max_val_grp_loss_index}, ")
if self.wandb_use:
d_wandb = {
"Iteration": step, "train_loss": train_loss,
"val_loss": val_loss, "grad_norm": grad_norm, "live_grad": live_grad,
"max_reward_err": max_reward_err , "max_reward_err_index": max_reward_err_index,
"max_kl_dist" : max_kl_dist, "max_kl_dist_index": max_kl_dist_index,
"max_train_grp_loss": max_train_grp_loss, "max_train_grp_loss_index": max_train_grp_loss_index,
"max_val_grp_loss": max_val_grp_loss, "max_val_grp_loss_index": max_val_grp_loss_index,
}
# Assuming rew_err is a list
for i, err in enumerate(rew_err):
key = f"reward_err_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = err
for i, param in enumerate(self.param):
key = f"reward_param_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = param
for i, grp_ls in enumerate(train_grp_loss):
key = f"train_group_loss_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = grp_ls
for i, grp_ls in enumerate(val_grp_loss):
key = f"val_group_loss_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = grp_ls
for i, kld in enumerate(kl_dist):
key = f"KL_distance_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = kld
wandb.log(d_wandb)
if self.logger:
self.logger.info(logging_str)
else:
print(logging_str)
rew = self.evaluate_reward(env, test_dataset)
return rew
def train(self, dataset: List[GroupTransition],
val_dataset: list[GroupTransition],
test_dataset: list[GroupTransition], env: GroupLinearBandit, optimal_reward: List[float]) -> float:
print(self.ipo_grad_type)
#print(dataset)
ratio=int(len(dataset)/self.batch_size)
# Collect unique group IDs using set comprehension
unique_group_ids = {transition.group_id for transition in dataset}
##count group numbers
group_counts = defaultdict(int)
# Iterate through the transitions and count occurrences
for group_transition in dataset:
print(group_transition)
group_counts[group_transition.group_id] += 1
# Sort the dictionary items by group_id
sorted_group_counts = sorted(group_counts.items(), key=lambda x: x[0])
# Extract the counts and convert them to a NumPy array
self.group_counts = np.array([count for _, count in sorted_group_counts])
# Convert the dictionary to a NumPy array
print(self.group_counts)
if self.importance_sampling==True:
self.weighted_batches=False
if self.importance_sampling_weights:
# process generalization adjustment stuff
imp_weights = [float(c) for c in self.importance_sampling_weights.split(',')]
assert len(imp_weights) == self.group_num
self.group_weights = np.array(imp_weights)
self.group_weights=self.group_weights/np.sum(self.group_weights)
else:
self.group_weights=np.array([1/count for count in self.group_counts])
self.group_weights=self.group_weights/np.sum(self.group_weights)
self.logger.info(f'unique_group_ids: {unique_group_ids}')
"""
step=-1
if self.ipo_grad_type=='justdpo':
train_loss = self.evaluate_loss(dataset)
val_loss = self.evaluate_loss(val_dataset)
train_wt_loss = self.evaluate_weighted_loss(dataset)
val_wt_loss = self.evaluate_weighted_loss(val_dataset)
train_grp_loss = self.evaluate_grp_loss(dataset)
val_grp_loss = self.evaluate_grp_loss(val_dataset)
grad_norm=self.evaluate_grad(dataset)
live_grad=grad_norm
else:
train_loss = self.evaluate_ipo_loss(dataset)
val_loss = self.evaluate_ipo_loss(val_dataset)
train_wt_loss = self.evaluate_weighted_ipo_loss(dataset)
val_wt_loss = self.evaluate_weighted_ipo_loss(val_dataset)
train_grp_loss = self.evaluate_ipo_grp_loss(dataset)
val_grp_loss = self.evaluate_ipo_grp_loss(val_dataset)
grad_norm=self.evaluate_ipo_grad(dataset)
live_grad=grad_norm
self.group_loss=train_grp_loss
kl_dist=self.evaluate_KL(env=env,states=test_dataset)
#Evaluate the reward on the test dataset:
#rew = self.evaluate_reward(env=env,
# states=test_dataset)
rew_err = [float(a - b)/a for a, b in zip(optimal_reward,self.evaluate_reward(env=env,
states=test_dataset) )]
formatted_rew=", ".join([f"{reward:.4f}" for reward in rew_err])
formatted_kl=", ".join([f"{kld:.4f}" for kld in kl_dist])
max_reward_err=max(rew_err)
max_reward_err_index=rew_err.index(max_reward_err)
max_kl_dist=max(kl_dist)
max_kl_dist_index=kl_dist.index(max_kl_dist)
max_train_grp_loss=np.max(train_grp_loss)
max_val_grp_loss=np.max(val_grp_loss)
max_cur_train_grp_loss=np.max(self.group_loss)
max_train_grp_loss_index=np.argmax(train_grp_loss)
max_val_grp_loss_index=np.argmax(val_grp_loss)
max_cur_train_grp_loss_index=np.argmax(self.group_loss)
logging_str = (f"Iteration: {step: d}, train_loss: {train_loss: .4f}, "
f"val_loss: {val_loss: .4f}, grad_norm: {grad_norm:.4f}, live_grad: {live_grad:.4f}, "
f"reward_err: {formatted_rew}, KL_dist: {formatted_kl}, param: {self.param}, weights: {self.group_weights}, "
f"train_wt_loss: {train_wt_loss: .4f}, val_wt_loss: {val_wt_loss:.4f}, "
f"train_grp_loss: {train_grp_loss}, val_grp_loss: {val_grp_loss}, "
f"train_hist_grp_loss: {self.hist_group_loss}, cur_train_grp_loss: {self.group_loss}, "
f"max_reward_err: {max_reward_err: .4f}, max_reward_err_index: {max_reward_err_index}, "
f"max_kl_dist: {max_kl_dist: .4f}, max_kl_dist_index: {max_kl_dist_index}, "
f"max_train_grp_loss: {max_train_grp_loss: .4f}, max_train_grp_loss_index: {max_train_grp_loss_index}, "
f"max_val_grp_loss: {max_val_grp_loss: .4f}, max_val_grp_loss_index: {max_val_grp_loss_index}, "
f"max_cur_train_grp_loss: {max_cur_train_grp_loss: .4f}, max_cur_train_grp_loss_index: {max_cur_train_grp_loss_index}, ")
if self.wandb_use:
d_wandb = {
"Iteration": step, "train_loss": train_loss,
"val_loss": val_loss, "grad_norm": grad_norm, "live_grad": live_grad,
"train_weighted_loss": train_wt_loss, "val_weighted_loss": val_wt_loss,
"max_reward_err": max_reward_err , "max_reward_err_index": max_reward_err_index,
"max_kl_dist" : max_kl_dist, "max_kl_dist_index": max_kl_dist_index,
"max_train_grp_loss": max_train_grp_loss, "max_train_grp_loss_index": max_train_grp_loss_index,
"max_val_grp_loss": max_val_grp_loss, "max_val_grp_loss_index": max_val_grp_loss_index,
"max_cur_train_grp_loss": max_cur_train_grp_loss, "max_cur_train_grp_loss_index": max_cur_train_grp_loss_index
}
# Assuming rew_err is a list
for i, err in enumerate(rew_err):
key = f"reward_err_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = err
for i, kld in enumerate(kl_dist):
key = f"KL_distance_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = kld
for i, param in enumerate(self.param):
key = f"reward_param_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = param
for i, grp_wt in enumerate(self.group_weights):
key = f"group_weight_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = grp_wt
for i, hist_grp_ls in enumerate(self.hist_group_loss):
key = f"hist_group_loss_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = hist_grp_ls
for i, grp_ls in enumerate(self.group_loss):
key = f"cur_group_loss_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = grp_ls
for i, grp_ls in enumerate(train_grp_loss):
key = f"train_group_loss_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = grp_ls
for i, grp_ls in enumerate(val_grp_loss):
key = f"val_group_loss_{i + 1}" # Creating dynamic key, e.g., "reward_err_1", "reward_err_2", ...
d_wandb[key] = grp_ls
wandb.log(d_wandb)
if self.logger:
self.logger.info(logging_str)
else:
print(logging_str)
"""
for step in range(ratio*self.num_iters):
if self.use_closed_form:
grad_norm,live_grad = self.batch_update_closed_form(dataset,self.batch_size,unique_group_ids)
else:
grad_norm,live_grad = self.batch_update_once(dataset,self.batch_size,unique_group_ids)
if step % 2000 == 0:
if self.ipo_grad_type=='justdpo':
train_loss = self.evaluate_loss(dataset)
val_loss = self.evaluate_loss(val_dataset)
else:
train_loss = self.evaluate_ipo_loss(dataset)
val_loss = self.evaluate_ipo_loss(val_dataset)
if self.ipo_grad_type=='justdpo':
train_wt_loss = self.evaluate_weighted_loss(dataset)
val_wt_loss = self.evaluate_weighted_loss(val_dataset)
else:
train_wt_loss = self.evaluate_weighted_ipo_loss(dataset)
val_wt_loss = self.evaluate_weighted_ipo_loss(val_dataset)
if self.ipo_grad_type=='justdpo':
train_grp_loss = self.evaluate_grp_loss(dataset)
val_grp_loss = self.evaluate_grp_loss(val_dataset)
else:
train_grp_loss = self.evaluate_ipo_grp_loss(dataset)
val_grp_loss = self.evaluate_ipo_grp_loss(val_dataset)
kl_dist=self.evaluate_KL(env=env,states=test_dataset)
#Evaluate the reward on the test dataset:
#rew = self.evaluate_reward(env=env,
# states=test_dataset)
rew_err = [float(a - b)/a for a, b in zip(optimal_reward,self.evaluate_reward(env=env,
states=test_dataset) )]
formatted_rew=", ".join([f"{reward:.4f}" for reward in rew_err])
formatted_kl=", ".join([f"{kld:.4f}" for kld in kl_dist])
max_reward_err=max(rew_err)