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When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?

Aviral Kumar, Joey Hong, Anikait Singh, Sergey Levine

TL;DR

The paper analyzes when offline RL can outperform behavioral cloning even with expert-like data, offering a theoretical characterization based on data coverage (C^*), horizon structure, and the presence of critical states. It derives bounds showing offline RL can beat BC under horizon-sparse rewards or limited critical states, and it demonstrates that noisy/suboptimal data can further favor offline RL on long-horizon tasks. Empirical results across gridworlds, robotic manipulation, navigation, and Atari corroborate the theory and highlight practical tuning considerations. Overall, the work provides guidance on when to apply offline RL versus BC and shows that, under common conditions, offline RL can yield substantial gains with the same data budget.

Abstract

Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing previously collected experience, without any online interaction. It is widely understood that offline RL is able to extract good policies even from highly suboptimal data, a scenario where imitation learning finds suboptimal solutions that do not improve over the demonstrator that generated the dataset. However, another common use case for practitioners is to learn from data that resembles demonstrations. In this case, one can choose to apply offline RL, but can also use behavioral cloning (BC) algorithms, which mimic a subset of the dataset via supervised learning. Therefore, it seems natural to ask: when can an offline RL method outperform BC with an equal amount of expert data, even when BC is a natural choice? To answer this question, we characterize the properties of environments that allow offline RL methods to perform better than BC methods, even when only provided with expert data. Additionally, we show that policies trained on sufficiently noisy suboptimal data can attain better performance than even BC algorithms with expert data, especially on long-horizon problems. We validate our theoretical results via extensive experiments on both diagnostic and high-dimensional domains including robotic manipulation, maze navigation, and Atari games, with a variety of data distributions. We observe that, under specific but common conditions such as sparse rewards or noisy data sources, modern offline RL methods can significantly outperform BC.

When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?

TL;DR

The paper analyzes when offline RL can outperform behavioral cloning even with expert-like data, offering a theoretical characterization based on data coverage (C^*), horizon structure, and the presence of critical states. It derives bounds showing offline RL can beat BC under horizon-sparse rewards or limited critical states, and it demonstrates that noisy/suboptimal data can further favor offline RL on long-horizon tasks. Empirical results across gridworlds, robotic manipulation, navigation, and Atari corroborate the theory and highlight practical tuning considerations. Overall, the work provides guidance on when to apply offline RL versus BC and shows that, under common conditions, offline RL can yield substantial gains with the same data budget.

Abstract

Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing previously collected experience, without any online interaction. It is widely understood that offline RL is able to extract good policies even from highly suboptimal data, a scenario where imitation learning finds suboptimal solutions that do not improve over the demonstrator that generated the dataset. However, another common use case for practitioners is to learn from data that resembles demonstrations. In this case, one can choose to apply offline RL, but can also use behavioral cloning (BC) algorithms, which mimic a subset of the dataset via supervised learning. Therefore, it seems natural to ask: when can an offline RL method outperform BC with an equal amount of expert data, even when BC is a natural choice? To answer this question, we characterize the properties of environments that allow offline RL methods to perform better than BC methods, even when only provided with expert data. Additionally, we show that policies trained on sufficiently noisy suboptimal data can attain better performance than even BC algorithms with expert data, especially on long-horizon problems. We validate our theoretical results via extensive experiments on both diagnostic and high-dimensional domains including robotic manipulation, maze navigation, and Atari games, with a variety of data distributions. We observe that, under specific but common conditions such as sparse rewards or noisy data sources, modern offline RL methods can significantly outperform BC.
Paper Structure (37 sections, 20 theorems, 84 equations, 9 figures, 4 tables, 2 algorithms)

This paper contains 37 sections, 20 theorems, 84 equations, 9 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Setup and Preliminaries
  4. Theoretical Comparison of BC and Offline RL
  5. Improved Performance Guarantees of BC and Offline RL
  6. Comparison Under Expert Data
  7. Comparison Under Noisy Data
  8. Comparison of Generalized BC Methods and Offline RL
  9. Empirical Evaluation of BC and Offline RL
  10. Discussion
  11. Pseudocode for Algorithms
  12. Proofs
  13. Proof of Theorem \ref{['thm:bc']}
  14. Proof of Theorem \ref{['thm:lcb_rl']}
  15. Technical Lemmas
  16. ...and 22 more sections

Key Result

Theorem 4.1

Under Conditions assumption:concentrability and assumption:value_bounded, the suboptimality of BC satisfies

Figures (9)

  • Figure 1: Illustration showing the intuition behind critical states. The agent is supposed to navigate to a high-reward region marked as the yellow polygon, without crashing into the walls. For different states, A, B and C that we consider, the agent has a high volume of actions that allow it to reach the goal at states A and C, but only few actions that allow it to do so at state B. States around A and C are not critical, and so this task has only a small volume of critical states (i.e., those in the thin tunnel).
  • Figure 2: Illustration showing the intuition behind noisy data. BC trained on expert data (data composition is shown on the left) may diverge away from the expert and find a poor policy that does not solve the task. On the other hand, if instead of expert data, offline RL is provided with noisy expert data that sometimes ventures away from the expert distribution, RL can use this data to learn to stay on the course to the goal.
  • Figure 3: Offline RL vs BC on gridworld domains.Left: We compare offline RL and BC on three different gridworlds with varying number of critical points for expert and near-expert data. Right: Taking the "Multiple Critical" domain, we examine the effect of increasing the noisiness of the dataset by interpolating it with one generated by a random policy, and show that RL improves drastically with increased noise over BC.
  • Figure 4: IQM performance of various algorithms evaluated on 7 Atari games under various dataset compositions (per game scores in Table \ref{['app:full_results']}). Note that offline-tuned CQL with expert data ("Tuned CQL") outperforms cloning the expert data ("BC (Expert)"), even though naïve CQL is comparable to BC in this setting. When CQL is provided with noisy-expert data, it significantly outperforms cloning the expert policy.
  • Figure 5: (Figure \ref{['fig:navigation_example']} restated) Illustration showing the intuition behind critical points in a navigation task. The agent is supposed to navigate to a high-reward region marked as the yellow polygon, without crashing into the walls. For different states, A, B and C that we consider, the agent has a high volume of actions that allow it to reach the goal at states A and C, but only few actions that allow it to do so at state B. States around A and C are not critical, and so this task has only a small volume of critical states (i.e., those in the thin tunnel).
  • ...and 4 more figures

Theorems & Definitions (33)

  • Theorem 4.1: Performance of BC
  • Theorem 4.2: Performance of conservative offline RL
  • Theorem 4.3: Information-theoretic lower-bound for offline learning with $C^* = 1$
  • Definition 4.1: Non-critical states
  • Corollary 4.1: Offline RL vs BC with critical states
  • Corollary 4.2: Performance of conservative offline RL with noisy data
  • Theorem 4.4: One-step is worse than $k$-step policy improvement
  • Lemma B.1: Theorem 4.4, rajaraman2020toward
  • Lemma B.2: Bernstein's inequality
  • Lemma B.3: Theorem 4, maurer2009bernstein
  • ...and 23 more