Abstract: This paper presents an explainable deep-reinforcement learning (DRL)-based safety-aware optimal adaptive tracking (SOAT) scheme for a class of nonlinear discrete-time (DT) affine systems ...
New benchmark shows top LLMs achieve only 29% pass rate on OpenTelemetry instrumentation, exposing the gap between ...
Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...
Building upon our previous work InftyThink, we introduce InftyThink+, an end-to-end reinforcement learning framework that directly optimizes the complete iterative reasoning trajectory. Building on ...
REC-R1 is a general framework that bridges generative large language models (LLMs) and recommendation systems via reinforcement learning. Check the paper here.
Reinforcement learning (RL) provides a computational framework in which an agent learns optimal policies by interacting with the environment and receiving feedback in the form of rewards (Sutton and ...
Abstract: Offline reinforcement learning (RL) learns policies from fixed-size datasets without interacting with the environment, while multi-agent reinforcement learning (MARL) faces challenges from ...
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