By categorizing and filtering user input, you can better focus on driving AI improvement. This iterative process—blending automation with human review—ensures AI learns from high-quality data, leading ...

Deep Reinforcement Learning for mobile robot navigation in IR-SIM simulation. Using DRL (SAC, TD3, PPO, DDPG) neural networks, a robot learns to navigate to a random goal point in a simulated ...

This law enables working professionals to earn academic degrees by recognizing prior learning and work experience. By providing an alternative pathway to formal education, the law aims to make ...

A benchmark for evaluating reinforcement learning algorithms that train the policies using both real data and imaginary rollouts from LLMs. The concept of imaginary rollouts was proposed by KALM ...

Abstract: Reinforcement learning (RL) has demonstrated exceptional performance ... Specifically, effectively blocking transitions to failure states, maintaining consistent policy action selection, and ...

The bloc’s economic competitiveness tsar last month suggested the EU was prepared to water down some of its green policies to placate the bloc’s industry. Speaking at a meeting of trade body ...

To leverage the strengths of both approaches, we introduce Hybrid pOlicy Path plannEr (HOPE). This novel solution integrates a reinforcement learning agent with Reeds-Shepp curves, enabling effective ...

The World Health Organization (WHO) today launched new guidance to help all countries reform and strengthen mental health policies and systems. Mental health services worldwide remain underfunded ...

With this transition information, the system can better estimate the states to assist the decision making." The new reinforcement learning framework Teng and his colleagues developed could soon open ...