Value functions are crucial in deep reinforcement learning, employing neural networks to align with target values. Challenges arise when upscaling value-based RL methods for extensive networks, like high-capacity Transformers, with regression. Researchers from Google DeepMind propose utilizing categorical cross-entropy loss, showing substantial improvements in scalability and performance over conventional regression approaches.
Value Functions in Deep Reinforcement Learning
Value functions are a crucial part of deep reinforcement learning (RL). They are implemented using neural networks and are trained through mean squared error regression to match bootstrapped target values. However, scaling up value-based RL methods for extensive networks, like high-capacity Transformers, has been challenging.
Challenges and Solutions
In supervised learning, leveraging cross-entropy classification loss enables reliable scaling to vast networks. Researchers have addressed this problem by exploring methods for training value functions with categorical cross-entropy loss in deep RL. This approach has shown substantial enhancements in performance, robustness, and scalability compared to conventional regression-based methods.
Research Findings
The HL-Gauss approach, in particular, has yielded significant improvements across diverse tasks and domains. It transforms the regression problem in TD learning into a classification problem, effectively addressing challenges in deep RL and offering valuable insights into more effective learning algorithms.
Practical Implications
Experiments demonstrate that a cross-entropy loss, HL-Gauss, consistently outperforms traditional regression losses like MSE across various domains. It shows improved performance, scalability, and sample efficiency, indicating its efficacy in training value-based deep RL models. HL-Gauss also enables better scaling with larger networks and achieves superior results compared to regression-based and distributional RL approaches.
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Conclusion
Reframing regression as classification and minimizing categorical cross-entropy, rather than mean squared error, leads to significant enhancements in performance and scalability across various tasks and neural network architectures in value-based RL methods. These improvements result from the cross-entropy loss’s capacity to facilitate more expressive representations and effectively manage noise and nonstationarity.
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