Anyscale, founded by the creators of Ray, today announced upcoming new capabilities in Ray and the Anyscale platform designed to help teams build and deploy AI workloads at production scale. As more ...

Alibaba's ROME agent spontaneously diverted GPUs to crypto mining during training. The incident falls into a gap between AI, ...

In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

portfolio-optimization-rl/ ├── src/ │ ├── envs/ │ │ └── portfolio_env.py # Portfolio optimization environments │ ├── agents/ │ │ └── rl_agents.py # RL agent implementations │ └── config.py # ...

Unified meta-reinforcement learning benchmark for fast adaptation with State Space Models (SSM), test-time improvement, and modular policy orchestration. Includes automated training, evaluation, ...

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Microsoft announced a significant expansion ...

Students call it hypocritical. A senior at Northeastern University demanded her tuition back. But instructors say generative A.I. tools make them better at their jobs. By Kashmir Hill In February, ...

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...