Learning the Supports for Categorical Critic in Reinforcement Learning

2026-07-02 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, medium

Summary

A novel approach to address a key limitation in distributional reinforcement learning (RL) is presented in "Learning the Supports for Categorical Critic in Reinforcement Learning." While traditional RL minimizes mean squared error for value functions, distributional RL models return distributions, often using techniques like Gaussian Histogram Loss (HL-Gauss). A significant challenge with HL-Gauss is the necessity to pre-define a fixed support interval, which is problematic given the non-stationary and stochastic nature of RL target values. This work proposes dynamically learning the lower and upper bounds of this support interval. The authors derive an objective that jointly learns these bounds alongside the categorical representation of scalar values, demonstrating it forms a tighter upper bound on the mean-squared Bellman error compared to non-learned HL-Gauss supports. Empirically, this objective ensures stable support interval adaptation, matching HL-Gauss performance on most continuous-control tasks and improving on a subset, all without requiring a pre-specified interval.

Key takeaway

For Machine Learning Engineers developing deep reinforcement learning agents, especially in continuous control, you should consider implementing dynamically learned support intervals for categorical critics. This approach eliminates the brittle hyperparameter tuning associated with pre-defined fixed supports in methods like HL-Gauss, offering stable adaptation and potentially improved performance. Evaluate this method to simplify your model design and enhance robustness in stochastic environments.

Key insights

Dynamically learning support bounds for categorical critics in distributional RL addresses fixed-interval limitations, improving performance and stability.

Principles

• Dynamic support learning tightens Bellman error bounds.
• Fixed support intervals hinder distributional RL.

Method

An objective is derived to jointly learn the lower and upper bounds of the support interval while simultaneously learning the categorical representation of scalar values, forming an upper bound on the mean-squared Bellman error.

In practice

• Achieve stable support interval adaptation.
• Improve or match HL-Gauss on continuous control.
• Eliminate need for pre-specified support intervals.

Topics

• Reinforcement Learning
• Distributional RL
• Categorical Critic
• Support Interval Learning
• Continuous Control
• Bellman Error

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.