arXiv:2607.01880v1 Announce Type: new Abstract: Value functions are an essential component in actor-critic based deep reinforcement learning (RL). Conventionally, these functions are trained as a regression task by minimising the mean squared error (MSE) relative to bootstrapped target values. Meanwhile, in distributional RL, a distribution of returns is modelled based on the distributional Bellman operator. This work investigates the Gaussian Histogram Loss (HL-Gauss), a recent approach that reframes value estimation as classification by encoding each scalar Bellman target as a Gaussian-smoot
The continuous evolution of deep reinforcement learning demands ongoing refinement of core algorithms to enhance stability and performance.
Improved value estimation methods can lead to more robust and sample-efficient reinforcement learning agents, impacting a wide range of AI applications.
This research introduces an alternative approach to value function approximation, moving from regression to classification, which could lead to more stable and effective training.
- · AI researchers
- · Reinforcement Learning practitioners
- · Robotics
- · Autonomous systems
- · Less efficient RL methods
- · Domains heavily reliant on current regression-based value functions
Refinement of reinforcement learning algorithms through novel loss functions makes agents more capable.
More robust RL agents could accelerate deployment in complex real-world environments previously considered too challenging.
Increased sophistication of RL agents contributes to the broader development of autonomous AI systems, potentially impacting various industries.
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Read at arXiv cs.LG