Safe Real-World Reinforcement Learning for Mobile Agent Obstacle Avoidance
Collision avoidance is key for mobile robots and agents to operate safely in the real world. In this work, we present an efficient and effective collision avoidance system that combines real-world reinforcement learning (RL), search-based online trajectory planning, and automatic emergency intervention, e.g. automatic emergency braking (AEB). The goal of the RL is to learn effective search heuristics that speed up the search for collision-free trajectory and reduce the frequency of triggering automatic emergency interventions. This novel setup enables RL to learn safely and directly on mobile…
This paper was accepted at "Reinforcement Learning for Real Life" workshop at NeurIPS 2022. Advancements in reinforcement learning (RL) have inspired new directions in intelligent automation of network defense. However, many of these advancements have either outpaced their application to network security or have not considered the challenges associated with…
Long chain-of-thought (CoT) significantly enhances large language models' (LLM) reasoning capabilities. However, the extensive reasoning traces lead to inefficiencies and an increased time-to-first-token (TTFT). We propose a novel training paradigm that uses reinforcement learning (RL) to guide reasoning LLMs to interleave thinking and answering for multi-hop questions. We observe that…
This is a LoRA for FLUX Klein Base 9b. **Link: https://civitai.red/models/2381927/flux2-klein-base-9b-smartphone-snapshot-photo-reality-style** All infos on how…