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\begin{document}
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\title{Broad Critic Deep Actor Reinforcement Learning for Continuous Control}



\author[1]{Shiron Thalagala}%
\author[1]{Pak Kin Wong}%
\author[1]{Xiaozheng Wang}%
\author[1]{Tianang Sun}%
\affil[1]{Affiliation not available}%


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  \date{April 27, 2026}


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\begin{abstract}
In the domain of continuous control, deep reinforcement learning (DRL)
demonstrates promising results. However, the dependence of DRL on deep
neural networks (DNNs) results in the demand for extensive data and
increased computational cost. To address this issue, a novel hybrid
actor-critic reinforcement learning (RL) framework is introduced. The
proposed framework integrates the broad learning system (BLS) with DNN,
aiming to merge the strengths of both distinct architectural paradigms.
Specifically, the critic network employs BLS for rapid value estimation
via ridge regression, while the actor network retains the DNN structure
to optimize policy gradients. This hybrid design is generalizable and
can enhance existing actor-critic algorithms. To demonstrate its
versatility, the proposed framework is integrated into three widely used
actor-critic algorithms-- deep deterministic policy gradient (DDPG),
soft actor-critic (SAC), and twin delayed DDPG (TD3), resulting in
BLS-augmented variants. Experimental results reveal that all
BLS-enhanced versions surpass their original counterparts in terms of
training efficiency and accuracy. These improvements highlight the
suitability of the proposed framework for real-time control scenarios,
where computational efficiency and rapid adaptation are critical.%
\end{abstract}%



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