arXiv:2601.23075v2 Announce Type: replace Abstract: On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies, often leading to brittle optimization when gradients are noisy, and policy updates must be conservative. In this paper, we revisit actor policy representation as a first-class design choice for on-policy RL. We study discretized categorical actors, which represent each action dimension as a distribution over discrete bins and induce a policy objective analogous to c
The continuous evolution of reinforcement learning for complex control tasks necessitates more robust and interpretable policy representations to overcome limitations of standard Gaussian actors, pushing researchers to explore novel architectural approaches.
Improving the stability and performance of on-policy reinforcement learning can accelerate the development of more reliable autonomous systems and advanced AI agents across various domains.
This research introduces 'discretized categorical actors,' offering a potential paradigm shift in how on-policy RL agents represent and learn control policies, potentially leading to more stable and efficient learning.
- · AI researchers
- · Robotics developers
- · Autonomous systems integrators
- · Reinforcement learning platforms
- · Developers reliant on brittle Gaussian actor implementations
More stable and efficient training of reinforcement learning models for continuous control tasks.
Faster deployment of capable AI agents and robotic systems in real-world applications.
Enhanced reliability and safety of autonomous systems, potentially accelerating broader adoption and integration into critical infrastructure.
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