Q-Learning#
Main Idea#
Early in 2000, as a classic method of Reinforcement Learning, Q-learning was adapted to decision-making problems[1] and kept evolving with various extensions, such as penalized Q-learning [2]. Q-learning with finite decision points is mainly a regression modeling problem based on positing regression models for outcome at each decision point. The target of Q-learning is to find an optimal policy \(\pi\) that can maximize the expected reward received at the end of the final decision point. In other words, by training a model with the observed data, we hope to find an optimal policy to predict the optimal action for each individual to maximize rewards. For example, considering the motivating example Personalized Incentives, Q-learning aims to find the best policy to assign different incentives (\(A\)) to different users to optimize the return-on-investment (\(R\)). Overall, Q-learning is practical and easy to understand, as it allows straightforward implementation of diverse established regression methods.
Note that, we assume the action space is either binary (i.e., 0,1) or multinomial (i.e., A,B,C,D), and the outcome of interest R is continuous and non-negative, where the larger the \(R\) the better.
Algorithm Details#
For multistage cases, we apply a backward iterative approach, which means that we start from the final decision point T and work our way backward to the initial decision point. At the final step \(T\), it is again a standard regression modeling problem that is the same as what we did for the single decision point case. Particularly, we posit a model \(Q_{T}(h_{T},a_{T})\) for the expectation of potential outcome \(R(\bar{a}_T)\), and then the optimal policy at step \(T\) is derived as \(\text{arg max}_{\pi_{T}}Q_{T}(h_{T},\pi_{T}(h_{T}))\). For the decision point \(T-1\) till the decision point \(1\), a new term is introduced, which is the pseudo-outcome \(\tilde{R}_{t}\):
By doing so, the pseudo-outcome taking the delayed effect into account to help explore the optimal policy. Then, for each decision point \(t<T\), with the \(\tilde{R}_{t+1}\) calculated, we repeat the regression modeling step for \(\tilde{R}_{t+1}\). After obtaining the fitted model \(\hat{Q}_{t}(h_{t},a_{t},\hat{\boldsymbol{\beta}}_{t})\), the optimal policy is obtained as \(\arg \max_{\pi_{t}}Q_{t}(h_{t},\pi_{t}(h_{t}))\).
Key Steps#
Policy Learning:
At the final decision point \(t=T\), fitted a model \(Q_{T}(h_{T},a_{T},\boldsymbol{\beta}_{T})\);
For each individual \(i\), calculated the pseudo-outcome \(\tilde{R}_{Ti}=\text{max}_{\pi}\hat{Q}_{T}(h_{Ti},\pi(h_{Ti}),\hat{\boldsymbol{\beta}}_{T})\), and the optimal action \(d^{opt}_{T}(\boldsymbol{s}_{i})=\text{arg max}_{a}\hat{Q}_{T}(h_{Ti},a,\hat{\boldsymbol{\beta}}_{T})\);
For decision point \(t = T-1,\cdots, 1\),
fitted a model \(\hat{Q}_{t}(h_{t},a_{t},\hat{\boldsymbol{\beta}}_{t})\) for the pseudo-outcome \(\tilde{R}_{t+1}\)
For each individual \(i\), calculated the pseudo-outcome \(\tilde{R}_{ti}=\text{max}_{\pi}\hat{Q}_{t}(h_{ti},\pi(h_{ti}),\hat{\boldsymbol{\beta}}_{t})\), and the optimal action \(d^{opt}_{t}(\boldsymbol{s}_{i})=\text{arg max}_{a}\hat{Q}_{t}(h_{ti},a,\hat{\boldsymbol{\beta}}_{t})\);
Policy Evaluation:
We use the backward iteration as what we did in policy learning. However, here for each round, the pseudo outcome is not the maximum of Q values. Instead, the pseudo outcome at decision point t is defined as below:
The estimated value of the policy is then the average of \(\tilde{R}_{1i}\).
Note we also provide an option for bootstrapping. Particularly, for a given policy, we utilize bootstrap resampling to get the estimated value of the regime and the corresponding estimated standard error.
Demo Code#
In the following, we exhibit how to apply the learner on real data to do policy learning and policy evaluation, respectively.
1. Policy Learning#
# TODO: feasible set
from causaldm.learners.CPL13.disc import QLearning
from causaldm.test import shared_simulation
import numpy as np
#prepare the dataset (dataset from the DTR book)
import pandas as pd
#Important!! reset the index is required
dataMDP = pd.read_csv("dataMDP_feasible.txt", sep=',')#.reset_index(drop=True)
R = dataMDP['Y']
S = dataMDP[['CD4_0','CD4_6','CD4_12']]
A = dataMDP[['A1','A2','A3']]
# initialize the learner
QLearn = QLearning.QLearning()
# specify the model you would like to use
# If want to include all the variable in S and A with no specific model structure, then use "Y~."
# Otherwise, specify the model structure by hand
# Note: if the action space is not binary, use C(A) in the model instead of A
model_info = [{"model": "Y~CD4_0+A1+CD4_0*A1",
'action_space':{'A1':[0,1]}},
{"model": "Y~CD4_0+CD4_6+A2+CD4_6*A2",
'action_space':{'A2':[0,1]}},
{"model": "Y~CD4_0+CD4_6+CD4_12+A3+CD4_12*A3",
'action_space':{'A3':[0,1]}}]
# train the policy
QLearn.train(S, A, R, model_info, T=3)
{2: <statsmodels.regression.linear_model.RegressionResultsWrapper at 0x22080dee3d0>,
1: <statsmodels.regression.linear_model.RegressionResultsWrapper at 0x22080e1a940>,
0: <statsmodels.regression.linear_model.RegressionResultsWrapper at 0x220819a82b0>}
#4. recommend action
opt_d = QLearn.recommend_action(S).value_counts()
#5. get the estimated value of the optimal regime
V_hat = QLearn.predict_value(S)
print("fitted model Q0:",QLearn.fitted_model[0].params)
print("fitted model Q1:",QLearn.fitted_model[1].params)
print("fitted model Q2:",QLearn.fitted_model[2].params)
print("opt regime:",opt_d)
print("opt value:",V_hat)
fitted model Q0: Intercept 167.898024
CD4_0 2.102009
A1 -1.116478
CD4_0:A1 0.002859
dtype: float64
fitted model Q1: Intercept 171.676661
CD4_0 2.454044
CD4_6 -0.288382
A2 -8.921595
CD4_6:A2 0.015938
dtype: float64
fitted model Q2: Intercept 158.553900
CD4_0 2.477566
CD4_6 -0.551396
CD4_12 0.334465
A3 182.312429
CD4_12:A3 -0.703112
dtype: float64
opt regime: A3 A2 A1
0 1 1 550
0 1 450
dtype: int64
opt value: 1113.3004201781755
QLearn.recommend_action(S).value_counts()
A3 A2 A1
0 1 1 550
0 1 450
dtype: int64
# Optional: we also provide a bootstrap standard deviaiton of the optimal value estimation
# Warning: results amay not be reliable
QLearn = QLearning.QLearning()
model_info = [{"model": "Y~CD4_0+A1+CD4_0*A1",
'action_space':{'A1':[0,1]}},
{"model": "Y~CD4_0+CD4_6+A2+CD4_0*A2+CD4_6*A2",
'action_space':{'A2':[0,1]}},
{"model": "Y~CD4_0+CD4_6+CD4_12+A3+CD4_0*A3+CD4_6*A3+CD4_12*A3",
'action_space':{'A3':[0,1]}}]
QLearn.train(S, A, R, model_info, T=3, bootstrap = True, n_bs = 200)
fitted_params,fitted_value,value_avg,value_std,params=QLearn.predict_value_boots(S)
print('Value_hat:',value_avg,'Value_std:',value_std)
Value_hat: 1113.860420629956 Value_std: 3.900572995169583
2. Policy Evaluation#
#specify the fixed regime to be tested
# For example, regime d = 1 for all subjects at all decision points\
N=len(S)
# !! IMPORTANT: INDEX SHOULD BE THE SAME AS THAT OF THE S,R,A
regime = pd.DataFrame({'A1':[1]*N,
'A2':[1]*N,
'A3':[1]*N}).set_index(S.index)
#evaluate the regime
QLearn = QLearning.QLearning()
model_info = [{"model": "Y~CD4_0+A1+CD4_0*A1",
'action_space':{'A1':[0,1]}},
{"model": "Y~CD4_0+CD4_6+A2+CD4_6*A2",
'action_space':{'A2':[0,1]}},
{"model": "Y~CD4_0+CD4_6+CD4_12+A3+CD4_12*A3",
'action_space':{'A3':[0,1]}}]
QLearn.train(S, A, R, model_info, T=3, regime = regime, evaluate = True)
QLearn.predict_value(S)
979.4518636939476
# bootstrap average and the std of estimate value
QLearn.train(S, A, R, model_info, T=3, regime = regime, evaluate = True, bootstrap = True, n_bs = 200)
fitted_params,fitted_value,value_avg,value_std,params=QLearn.predict_value_boots(S)
print('Value_hat:',value_avg,'Value_std:',value_std)
Value_hat: 979.3385307224661 Value_std: 4.1985718821033515
💥 Placeholder for C.I.
References#
Murphy, S. A. (2005). A generalization error for Q-learning.
Song, R., Wang, W., Zeng, D., & Kosorok, M. R. (2015). Penalized q-learning for dynamic treatment regimens. Statistica Sinica, 25(3), 901.