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Revisiting Bellman Errors for Offline Model Selection
Machine Learning · Submitted 31 Jan 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our estimator obtains impressive OMS performance on diverse discrete control tasks, including Atari games.
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The abstract, sentence by sentence
Offline model selection (OMS), that is, choosing the best policy from a set of many policies given only logged data, is crucial for applying offline RL in real-world settings. One idea that has been extensively explored is to select policies based on the mean squared Bellman error (MSBE) of the associated Q-functions. However, previous work has struggled to obtain adequate OMS performance with Bellman errors, leading many researchers to abandon the idea. To this end, we elucidate why previous work has seen pessimistic results with Bellman errors and identify conditions under which OMS algorithms based on Bellman errors will perform well. Moreover, we develop a new estimator of the MSBE that is more accurate than prior methods. Our estimator obtains impressive OMS performance on diverse discrete control tasks, including Atari games.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Offline model selection (OMS), that is, choosing the best policy from a set of many policies given only logged data, is crucial for applying offline RL in real-world settings. | background 1.00 | 0.05 | 0.03 | asserted 1.00 |
| s1 | One idea that has been extensively explored is to select policies based on the mean squared Bellman error (MSBE) of the associated Q-functions. | background 1.00 | 0.04 | 0.03 | asserted 0.99 |
| s2 | However, previous work has struggled to obtain adequate OMS performance with Bellman errors, leading many researchers to abandon the idea. | background 0.50 | 0.03 | 0.03 | asserted 0.99 |
| s3 | To this end, we elucidate why previous work has seen pessimistic results with Bellman errors and identify conditions under which OMS algorithms based on Bellman errors will perform well. | contribution 0.99 | 0.91 | 0.15 | asserted 0.99 |
| s4 | Moreover, we develop a new estimator of the MSBE that is more accurate than prior methods. | contribution 0.71 | 0.96 | 0.11 | asserted 1.00 |
| s5 | Our estimator obtains impressive OMS performance on diverse discrete control tasks, including Atari games. | result 1.00 | 0.95 | 0.55 | asserted 1.00 |
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Original abstract
Offline model selection (OMS), that is, choosing the best policy from a set of many policies given only logged data, is crucial for applying offline RL in real-world settings. One idea that has been extensively explored is to select policies based on the mean squared Bellman error (MSBE) of the associated Q-functions. However, previous work has struggled to obtain adequate OMS performance with Bellman errors, leading many researchers to abandon the idea. To this end, we elucidate why previous work has seen pessimistic results with Bellman errors and identify conditions under which OMS algorithms based on Bellman errors will perform well. Moreover, we develop a new estimator of the MSBE that is more accurate than prior methods. Our estimator obtains impressive OMS performance on diverse discrete control tasks, including Atari games.
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What it cites · 8 sentences without a confident label
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The results and proof techniques of our first proposition, Proposition [ref], resembles those used by more recent theoretical work (e.g. [ref] ).
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency · Theoretical Analysis · 0.88
Cites as support 1 paper
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However, our results add to a growing body of work suggesting that improving the architecture and other hyperparameters may be quite important [ref] .
Behavior Regularized Offline Reinforcement Learning · Ablation Experiments · 0.96
Cites as background 1 paper
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We assume that the observed state-action pairs in $𝒟$ are identically distributed as $P^μ(s,a)=d^μ(s)μ(a|s)$ where $μ$ is the behavioral policy and $d^μ$ is the marginal distribution of states over time points induced by policy $μ$ and MDP $ℳ$ [ref] .
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems · Offline Reinforcement Learning · 0.84
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