arXiv:2606.04275v1 Announce Type: new Abstract: We present a novel theoretical framework for deep reinforcement learning (RL) in continuous environments by modeling the problem as a continuous-time stochastic process, drawing on insights from stochastic control. Building on previous work, we introduce a viable model of actor-critic algorithm that incorporates both exploration and stochastic transitions. For single-hidden-layer neural networks, we show that the state of the environment can be formulated as a two time scale process: the environment time and the gradient time. Within this formula
The paper builds on existing theoretical frameworks for deep reinforcement learning, pushing the boundaries of understanding continuous-time stochastic processes in AI at a time of rapid architectural innovation.
This theoretical breakthrough in continuous reinforcement learning could lead to more robust and generalized AI agents, impacting autonomous systems across various sectors and potentially accelerating advanced AI development.
The ability to model RL in continuous environments with insights from stochastic control, particularly the two time scale process, offers a more sophisticated approach to AI training and deployment.
- · AI research institutions
- · Robotics and autonomous systems developers
- · Semiconductor manufacturers
- · Cloud computing providers
- · Developers of less robust, discrete RL systems
Improved performance and stability in AI systems operating in dynamic, real-world continuous environments.
Accelerated development of sophisticated AI agents capable of complex decision-making and exploration in uncharted territories.
Enhanced automation across industries, potentially leading to increased demand for compute and specialized hardware to run these advanced agents.
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Read at arXiv cs.LG