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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.

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Citations (37)
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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.