Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
166 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL (2403.03950v1)

Published 6 Mar 2024 in cs.LG, cs.AI, and stat.ML

Abstract: Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained using a mean squared error regression objective to match bootstrapped target values. However, scaling value-based RL methods that use regression to large networks, such as high-capacity Transformers, has proven challenging. This difficulty is in stark contrast to supervised learning: by leveraging a cross-entropy classification loss, supervised methods have scaled reliably to massive networks. Observing this discrepancy, in this paper, we investigate whether the scalability of deep RL can also be improved simply by using classification in place of regression for training value functions. We demonstrate that value functions trained with categorical cross-entropy significantly improves performance and scalability in a variety of domains. These include: single-task RL on Atari 2600 games with SoftMoEs, multi-task RL on Atari with large-scale ResNets, robotic manipulation with Q-transformers, playing Chess without search, and a language-agent Wordle task with high-capacity Transformers, achieving state-of-the-art results on these domains. Through careful analysis, we show that the benefits of categorical cross-entropy primarily stem from its ability to mitigate issues inherent to value-based RL, such as noisy targets and non-stationarity. Overall, we argue that a simple shift to training value functions with categorical cross-entropy can yield substantial improvements in the scalability of deep RL at little-to-no cost.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (63)
  1. One-step distributional reinforcement learning. CoRR, abs/2304.14421, 2023.
  2. An optimistic perspective on offline reinforcement learning. In International Conference on Machine Learning (ICML), 2020.
  3. Deep reinforcement learning at the edge of the statistical precipice. Neural Information Processing Systems (NeurIPS), 2021.
  4. Investigating multi-task pretraining and generalization in reinforcement learning. In International Conference on Learning Representations (ICLR), 2023.
  5. The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research (JAIR), 47:253–279, 2013.
  6. A distributional perspective on reinforcement learning. In International Conference on Machine Learning (ICML), 2017.
  7. A geometric perspective on optimal representations for reinforcement learning. In Neural Information Processing Systems (NeurIPS), 2019.
  8. Distributional reinforcement learning. MIT Press, 2023.
  9. JAX: composable transformations of Python+NumPy programs, 2018. URL http://github.com/google/jax.
  10. Dopamine: A Research Framework for Deep Reinforcement Learning. CoRR, abs/1812.06110, 2018.
  11. Johan Samir Obando Ceron and Pablo Samuel Castro. Revisiting rainbow: Promoting more insightful and inclusive deep reinforcement learning research. In International Conference on Machine Learning (ICML), 2021.
  12. Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions. In Conference on Robot Learning (CoRL), 2023.
  13. The value-improvement path: Towards better representations for reinforcement learning. In AAAI Conference on Artificial Intelligence, 2021.
  14. Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures. In International Conference on Machine Learning (ICML), 2018.
  15. Generalization and regularization in DQN. CoRR, abs/1810.00123, 2018.
  16. Proto-value networks: Scaling representation learning with auxiliary tasks. In International Conference on Learning Representations (ICLR), 2023.
  17. Mastering diverse domains through world models. CoRR, abs/2301.04104, 2023.
  18. TD-MPC2: Scalable, robust world models for continuous control. In International Conference on Learning Representations (ICLR), 2024.
  19. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
  20. Momentum contrast for unsupervised visual representation learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
  21. Muesli: Combining improvements in policy optimization. In International Conference on Machine Learning (ICML), 2021.
  22. Retinagan: An object-aware approach to sim-to-real transfer. In IEEE International Conference on Robotics and Automation (ICRA), 2021.
  23. Improving regression performance with distributional losses. In International Conference on Machine Learning (ICML), 2018.
  24. Investigating the histogram loss in regression. CoRR, abs/2402.13425, 2024.
  25. Scaling laws for neural language models. CoRR, abs/2001.08361, 2020.
  26. End-to-end learning of geometry and context for deep stereo regression. In IEEE International Conference on Computer Vision (ICCV), 2017.
  27. Imagenet classification with deep convolutional neural networks. Neural Information Processing Systems (NeurIPS), 2012.
  28. Conservative q-learning for offline reinforcement learning. Neural Information Processing Systems (NeurIPS), 2020.
  29. Implicit under-parameterization inhibits data-efficient deep reinforcement learning. In International Conference on Learning Representations (ICLR), 2021.
  30. Dr3: Value-based deep reinforcement learning requires explicit regularization. In International Conference on Learning Representations (ICLR), 2022.
  31. Offline Q-Learning on Diverse Multi-Task Data Both Scales and Generalizes. In International Conference on Learning Representations (ICLR), 2023.
  32. On the generalization of representations in reinforcement learning. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
  33. Bootstrapped representations in reinforcement learning. In International Conference on Machine Learning (ICML), 2023.
  34. Multi-game decision transformers. In Neural Information Processing Systems (NeurIPS), 2022.
  35. Beyond a*: Better planning with transformers via search dynamics bootstrapping. CoRR, abs/2402.14083, 2024.
  36. Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems. CoRR, abs/2005.01643, 2020.
  37. A comparative analysis of expected and distributional reinforcement learning. In AAAI Conference on Artificial Intelligence, 2019.
  38. On the effect of auxiliary tasks on representation dynamics. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
  39. Understanding and preventing capacity loss in reinforcement learning. In International Conference on Learning Representations (ICLR), 2022.
  40. Disentangling the causes of plasticity loss in neural networks. CoRR, abs/2402.18762, 2024.
  41. Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents. Journal of Artificial Intelligence Research (JAIR), 61:523–562, 2018.
  42. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.
  43. Asynchronous methods for deep reinforcement learning. In International Conference on Machine Learning (ICML), 2016.
  44. Mixtures of experts unlock parameter scaling for deep rl. CoRR, abs/2402.08609, 2024.
  45. Pytorch: An imperative style, high-performance deep learning library. In Neural Information Processing Systems (NeurIPS), 2019.
  46. A step towards understanding why classification helps regression. In IEEE International Conference on Computer Vision (ICCV), pages 19972–19981, 2023.
  47. From sparse to soft mixtures of experts. In International Conference on Learning Representations (ICLR), 2024.
  48. Lcr-net++: Multi-person 2d and 3d pose detection in natural images. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 42(5):1146–1161, 2019.
  49. Deep expectation of real and apparent age from a single image without facial landmarks. International Journal of Computer Vision (IJCV), 126(2-4):144–157, 2018.
  50. The statistical benefits of quantile temporal-difference learning for value estimation. In International Conference on Machine Learning (ICML), 2023.
  51. Grandmaster-level chess without search. CoRR, abs/2402.04494, 2024.
  52. Mastering atari, go, chess and shogi by planning with a learned model. Nature, 588(7839):604–609, 2020.
  53. Mastering the game of go without human knowledge. Nature, 550(7676):354–359, 2017.
  54. Offline RL for natural language generation with implicit language q learning. In International Conference on Learning Representations (ICLR), 2023.
  55. Offline actor-critic reinforcement learning scales to large models. CoRR, abs/2402.05546, 2024.
  56. Regression as classification: Influence of task formulation on neural network features. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
  57. Rethinking the inception architecture for computer vision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
  58. Regression by classification. In Brazilian Symposium on Artificial Intelligence, pages 51–60. Springer, 1996.
  59. Pixel recurrent neural networks. In International Conference on Machine Learning (ICML), 2016.
  60. Attention is all you need. Neural Information Processing Systems (NeurIPS), 2017.
  61. The benefits of being distributional: Small-loss bounds for reinforcement learning. In Neural Information Processing Systems (NeurIPS), 2023.
  62. Rule-based machine learning methods for functional prediction. Journal of Artificial Intelligence Research (JAIR), 3:383–403, 1995.
  63. Improving deep regression with ordinal entropy. In International Conference on Learning Representations (ICLR), 2023.
Citations (37)
View on Semantic Scholar

Summary

  • The paper demonstrates that training value functions via classification yields robust improvements over conventional MSE regression in deep reinforcement learning.
  • It details a categorical representation using cross-entropy loss, especially HL-Gauss, to mitigate issues like noisy targets and non-stationarity.
  • Empirical results across Atari, robotics, chess, and language tasks validate the approach’s state-of-the-art performance and scalability.

Training Value Functions via Classification: A Novel Approach for Enhancing Deep Reinforcement Learning

Introduction to the Paper's Contribution

Recent advancements in deep learning have prominently showcased the effectiveness of classification problems for training large neural networks. Despite the natural inclination towards regression-based methods within reinforcement learning (RL) for value function approximation, this paper presents compelling evidence suggesting a shift towards adopting classification in lieu of regression could significantly bolster deep RL's performance and scalability. Titled "Stop Regressing: Training Value Functions via Classification for Scalable Deep RL", the paper exhaustively explores the impact of training value functions—integral components of deep RL—using categorical cross-entropy as opposed to the conventional mean squared error (MSE) regression objective. The paper's extensive evaluations span across various domains, including single and multi-task RL on Atari games, robotic manipulation, Chess, and a language-agent task on Wordle, achieving state-of-the-art results across these benchmarks.

Methodological Overview

Categorical Representation of Value Functions

The paper proposes representing value functions as categorical distributions over discretized value ranges, instead of continuous values. This representation aligns with the classical view of regression as a special case of classification, where targets are discretized into categories, and predictions are made by calculating the expected value of these distributions. Remarkably, adopting such a representation mitigates common RL challenges, including noisy targets and non-stationarity, through the inherently stable gradient updates and ordinal nature utilized in classification losses.

Categorical Cross-Entropy Loss for RL

At the core of the methodology is the training of value functions with categorical cross-entropy loss. The paper investigates various methods to construct and project scalar regression targets onto categorical distributions supported by a fixed set of discrete classes. Among these methods, the Histogram Loss augmented with Gaussian smoothing (HL-Gauss) consistently outperforms others by leveraging its capability to distribute probability mass to neighboring bins—effectively utilizing the ordinal structure of the regression problem. The empirical evaluation across diverse domains underscores the superiority of HL-Gauss over the MSE regression loss, indicating a compelling direction for value function approximation in deep RL.

Empirical Evaluations

The paper conducts a comprehensive evaluation of the proposed classification approach against traditional regression-based methods across a series of domains:

  • Atari 2600 Games: Demonstrates significant improvements in both single-task and multi-task RL, with HL-Gauss outperforming MSE and even distributional RL methods like C51.
  • Robotic Manipulation with Transformers and Chess Playing without Search: Shows a substantial boost in performance, validating the approach's effectiveness beyond standard RL benchmarks.
  • Language-Agent Task on Wordle: Further evidences the universal applicability of training RL agents with classification, achieving remarkable success rates.

Understanding the Benefits

The analysis explores why classification outperforms regression in value-based RL, highlighting the following insights:

  • Classification methods, particularly HL-Gauss, are inherently more robust to the noisy and non-stationary nature of RL environments.
  • The improved robustness and stability stem from the grounded gradient updates and the distributed nature of probability mass across discrete classes, enabling more expressive and effective representation learning.
  • The approach exhibits less susceptibility to overfitting, maintaining high adaptability (plasticity) to evolving targets—an essential attribute for tackling non-stationarity in RL.

Looking Ahead

This paper's findings advocate for a paradigm shift in deep RL from regression to classification for training value functions. The improved performance, robustness, and scalability offered by classification methods bear significant implications for future RL research and algorithm design, potentially paving the way for more efficient and powerful RL agents capable of tackling an even broader spectrum of complex tasks. As deep RL continues to evolve, incorporating classification-based approaches could herald a new era of advancements, pushing the boundaries of what is achievable in artificial intelligence.