RECORD
Confidence-Conditioned Value Functions for Offline Reinforcement Learning
Machine Learning · Submitted 8 Dec 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Finally, we empirically show that our algorithm outperforms existing conservative offline RL algorithms on multiple discrete control domains.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
Offline reinforcement learning (RL) promises the ability to learn effective policies solely using existing, static datasets, without any costly online interaction. To do so, offline RL methods must handle distributional shift between the dataset and the learned policy. The most common approach is to learn conservative, or lower-bound, value functions, which underestimate the return of out-of-distribution (OOD) actions. However, such methods exhibit one notable drawback: policies optimized on such value functions can only behave according to a fixed, possibly suboptimal, degree of conservatism. However, this can be alleviated if we instead are able to learn policies for varying degrees of conservatism at training time and devise a method to dynamically choose one of them during evaluation. To do so, in this work, we propose learning value functions that additionally condition on the degree of conservatism, which we dub confidence-conditioned value functions. We derive a new form of a Bellman backup that simultaneously learns Q-values for any degree of confidence with high probability. By conditioning on confidence, our value functions enable adaptive strategies during online evaluation by controlling for confidence level using the history of observations thus far. This approach can be implemented in practice by conditioning the Q-function from existing conservative algorithms on the confidence.We theoretically show that our learned value functions produce conservative estimates of the true value at any desired confidence. Finally, we empirically show that our algorithm outperforms existing conservative offline RL algorithms on multiple discrete control domains.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Offline reinforcement learning (RL) promises the ability to learn effective policies solely using existing, static datasets, without any costly online interaction. | background 1.00 | 0.04 | 0.03 | hedged possibility 0.55 |
| s1 | To do so, offline RL methods must handle distributional shift between the dataset and the learned policy. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s2 | The most common approach is to learn conservative, or lower-bound, value functions, which underestimate the return of out-of-distribution (OOD) actions. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s3 | However, such methods exhibit one notable drawback: policies optimized on such value functions can only behave according to a fixed, possibly suboptimal, degree of conservatism. | gap 0.46 | 0.08 | 0.03 | asserted 0.98 |
| s4 | However, this can be alleviated if we instead are able to learn policies for varying degrees of conservatism at training time and devise a method to dynamically choose one of them during evaluation. | gap 0.61 | 0.09 | 0.09 | hedged possibility 0.93 |
| s5 | To do so, in this work, we propose learning value functions that additionally condition on the degree of conservatism, which we dub confidence-conditioned value functions. | contribution 0.93 | 0.88 | 0.15 | asserted 0.98 |
| s6 | We derive a new form of a Bellman backup that simultaneously learns Q-values for any degree of confidence with high probability. | method 0.69 | 0.94 | 0.09 | asserted 1.00 |
| s7 | By conditioning on confidence, our value functions enable adaptive strategies during online evaluation by controlling for confidence level using the history of observations thus far. | method 0.79 | 0.73 | 0.12 | asserted 0.94 |
| s8 | This approach can be implemented in practice by conditioning the Q-function from existing conservative algorithms on the confidence.We theoretically show that our learned value functions produce conservative estimates of the true value at any desired confidence. | method 0.61 | 0.86 | 0.13 | asserted 0.96 |
| s9 | Finally, we empirically show that our algorithm outperforms existing conservative offline RL algorithms on multiple discrete control domains. | result 1.00 | 0.96 | 0.57 | asserted 1.00 |
What was examined, and what was not
- arXiv listing abstract v2
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
Offline reinforcement learning (RL) promises the ability to learn effective policies solely using existing, static datasets, without any costly online interaction. To do so, offline RL methods must handle distributional shift between the dataset and the learned policy. The most common approach is to learn conservative, or lower-bound, value functions, which underestimate the return of out-of-distribution (OOD) actions. However, such methods exhibit one notable drawback: policies optimized on such value functions can only behave according to a fixed, possibly suboptimal, degree of conservatism. However, this can be alleviated if we instead are able to learn policies for varying degrees of conservatism at training time and devise a method to dynamically choose one of them during evaluation. To do so, in this work, we propose learning value functions that additionally condition on the degree of conservatism, which we dub confidence-conditioned value functions. We derive a new form of a Bellman backup that simultaneously learns Q-values for any degree of confidence with high probability. By conditioning on confidence, our value functions enable adaptive strategies during online evaluation by controlling for confidence level using the history of observations thus far. This approach can be implemented in practice by conditioning the Q-function from existing conservative algorithms on the confidence.We theoretically show that our learned value functions produce conservative estimates of the true value at any desired confidence. Finally, we empirically show that our algorithm outperforms existing conservative offline RL algorithms on multiple discrete control domains.
Human review
How the literature uses it
10 arXiv papers cite this in 11 sentences
Papers citing it · 8 sentences without a confident label
Cites it as background 3 papers
-
Additionally, other research endeavors explore offline adaptation methods to mitigate environmental uncertainty by modelling a posterior distribution over all possible Markov Decision Processes (MDPs) [ref] or considering varying confidence levels in conservative value estimates [ref] .
An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning · Related Work · 0.92
-
Furthermore, count-based techniques [ref] are employed in offline RL to do anti-exploration.
Exploration and Anti-Exploration with Distributional Random Network Distillation · Related Work · 0.85
-
Offline RL can be broadly classified into four categories: policy constraint [ref] , value regularization [ref] , model-based method [ref] , and return-conditioned supervised learning [ref] .
A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective · Related Works · 0.98
What it cites · 17 sentences without a confident label
Uses the method or tool of 1 paper
-
Namely, we use network parameterizations studied in distributional RL to predict Q-values parameterized by confidence [ref] .
Implicit Quantile Networks for Distributional Reinforcement Learning · Introduction · 0.97
Compares with 1 paper
-
We compare to prior offline RL methods, REM [ref] and CQL [ref] , and ablations of our method where we either replace confidence-conditioning with a simple ensemble, which we dub adaptive ensemble value-learning (AEVL), or behave according to a fixed confidence online, which we call Fixed-CCVL.
Conservative Q-Learning for Offline Reinforcement Learning · Empirical Evaluation · 1.00
Cites as background 14 papers
-
On the other hand, conservative methods learn a lower-bound, or conservative, estimate of return and optimize the policy against it [ref] .
MOPO: Model-based Offline Policy Optimization · Related Work · 0.92
-
On the other hand, conservative methods learn a lower-bound, or conservative, estimate of return and optimize the policy against it [ref] .
Conservative Q-Learning for Offline Reinforcement Learning · Related Work · 0.92
-
On the other hand, conservative methods learn a lower-bound, or conservative, estimate of return and optimize the policy against it [ref] .
Offline Reinforcement Learning with Fisher Divergence Critic Regularization · Related Work · 0.92
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.