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Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions (2309.10150v2)

Published 18 Sep 2023 in cs.RO, cs.AI, and cs.LG

Abstract: In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal difference backups. We therefore refer to the method as Q-Transformer. By discretizing each action dimension and representing the Q-value of each action dimension as separate tokens, we can apply effective high-capacity sequence modeling techniques for Q-learning. We present several design decisions that enable good performance with offline RL training, and show that Q-Transformer outperforms prior offline RL algorithms and imitation learning techniques on a large diverse real-world robotic manipulation task suite. The project's website and videos can be found at https://qtransformer.github.io

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Q-Transformer: Autoregressive Q-Functions for Scalable Offline Reinforcement Learning

The paper introduces a novel method termed "Q-Transformer," which leverages transformer architectures to represent Q-functions in reinforcement learning (RL). This work focuses on scalable offline RL tailored for training policies from extensive multi-task datasets. This approach synthesizes Q-learning with leading sequence modeling techniques, promoting the execution of diverse manipulation tasks by robotic systems both from human demonstrations and autonomously collected data.

Methodology

Q-Transformer Approach:

  1. Autoregressive Modeling: In contrast to traditional methods, Q-Transformer discretizes each action dimension, treating them as separate tokens. This technique underpins autoregressive Q-functions that are beneficial for representing high-capacity sequence models, thereby enabling the training of complex policies.
  2. Temporal Difference Backups: Offline RL underlies the use of temporal difference backups for Q-function estimation. Transformers managed the task of transforming these backups into sequence models.
  3. Conservative Q-Function Regularizer: Q-Transformer incorporates a conservative regularizer which strategically controls distributional shifts in offline RL. This regularizer effectively penalizes out-of-distribution actions by minimizing their Q-values, maintaining robustness in value estimation.
  4. Monte Carlo and Multi-Step Returns: The implementation benefits from the inclusion of Monte Carlo returns while utilizing nn-step updates. These augmentations facilitate more efficient learning dynamics by expediting value propagation.

Experimental Evaluation

The framework was tested on large-scale, real-world robotic manipulation datasets involving multi-task challenges such as picking, placing, and navigating objects. The dataset includes 38,000 successful demonstrations, supplemented by 20,000 episodes of failed attempts demonstrating exploratory behavior from robots.

Results: The Q-Transformer exhibited superior performance compared to prior imitation learning algorithms and alternative offline RL methods. The contrasting success rates underscore the architecture’s ability to effectively synthesize and optimize information gleaned from both demonstration and autonomously collected datasets.

Implications and Future Directions

Practical Implications:

  • Robust Policy Learning: Q-Transformer promises an efficient learning methodology that can train policies capable of outperforming human teleoperators, enhancing the proficiency of robotic systems.
  • Scalability Across Environments: Its efficacy in handling diverse datasets promotes adaptability across various real-world tasks and environments, emphasizing deployment potential in robotics.

Theoretical Implications:

  • Transforming RL with High-Capacity Models: The successful integration of transformers into RL environments incites discussions on their broader applicability in other RL paradigms.
  • Advanced Conservative Mechanisms: The algorithm’s unique approach in mitigating distributional shifts presents a playful avenue for other RL methods aiming to achieve stability in offline settings.

Future Work:

  • Extended Real-World Applications: Further exploration is warranted in evaluating Q-Transformer’s ability to scale in high-dimensional control scenarios, such as humanoid robots, which may involve intricate action dynamics.
  • Online Adjustment and Finetuning: Investigating methods for online finetuning within the Q-Transformer framework could foster real-time policy improvements, accentuating autonomous capabilities.

The strategic melding of sequence modeling with Q-learning delineates a promising frontier for reinforcement learning, especially in the nuanced domain of robotic policy synthesis via extensive offline datasets. The advances poised by Q-Transformer ignite profound discussions on the architectural designs that can revolutionize RL frameworks in the context of robotics and beyond.

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Authors (25)
  1. Yevgen Chebotar (28 papers)
  2. Quan Vuong (41 papers)
  3. Alex Irpan (23 papers)
  4. Karol Hausman (56 papers)
  5. Fei Xia (111 papers)
  6. Yao Lu (212 papers)
  7. Aviral Kumar (74 papers)
  8. Tianhe Yu (36 papers)
  9. Alexander Herzog (32 papers)
  10. Karl Pertsch (35 papers)
  11. Keerthana Gopalakrishnan (14 papers)
  12. Julian Ibarz (26 papers)
  13. Ofir Nachum (64 papers)
  14. Sumedh Sontakke (4 papers)
  15. Grecia Salazar (3 papers)
  16. Huong T Tran (1 paper)
  17. Jodilyn Peralta (4 papers)
  18. Clayton Tan (4 papers)
  19. Deeksha Manjunath (2 papers)
  20. Jaspiar Singht (1 paper)
Citations (64)
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