reinforcement-learning

_Ieee Open Journal of the Communications Society_ 7. 2026Energy imbalance remains a key challenge in Wireless Sensor Networks (WSNs), as nodes near the base station deplete their energy faster due to heavy forwarding loads. While mobile agents (MAs) have been employed for either data collection or sensor charging, existing approaches lack adaptability and fail to integrate both functions under re…

Improving reinforcement learning for complex physics The post Dynamical System Transfer Learning with Reduced Order Models appeared first on Towards Data Science .

You built an agent that calls a model, picks a tool, and prints an answer. It works fine in a script. Then you try to run it for real, and it falls apart. No memory between steps. No retry when a call fails. No record of what actually happened. That's the moment every builder finds out they didn't build an agent. They built a function pretending to be one. What they actually needed was an agent r…

AI agents are easy to demo and surprisingly hard to evaluate. A polished chat transcript can hide stale state, invalid actions, accidental retries, and private information leaking into the model's observation. I built WagerCall as a bounded environment for studying those problems. Agents play casino-style simulations through the Model Context Protocol (MCP), but every balance is made of synthetic…

Scientific Reports, Published online: 04 September 2026; doi:10.1038/s41598-026-65000-x Adaptive rocket trajectory optimization via reinforcement learning and FPGA acceleration

You hide. An AI agent hunts you through this arena over WebMCP — it can't see your screen, only what its sensors report. Your glow, your scent trail, and every noise you make betray you. Move with WASD. Survive 3 minutes. Waiting for agent to start No agent yet? The built-in seeker can hunt you on its own. It plays by instinct and never rewrites itself.

AI agents are showing up everywhere—and most of them are being built by trial and error. Agent Design Patterns distills the experience of a growing community of agent builders into 20+ composable, reusable patterns for building AI agents that are reliable, efficient, controllable, and easy to reason about. In this uniquely valuable book, author and NVIDIA Research scientist Peter Belcak gives you…

Insider Brief PRESS RELEASE — Deep Cogito, a post-training research lab focused on reinforcement learning and self-improvement, announced a $43 million Series A led by TQ Ventures, with participation from Benchmark, Nexus Venture Partners, Atreides Management, South Park Commons, and Zscaler. The round brings Deep Cogito’s total funding to more than $56 million. Deep Cogito was founded […]

A single qutrit replicates genuine environments precisely, whereas any finite classical model incurs an average reward loss of at least ε and nominates suboptimal actions for over half of all possible trajectories. This demonstrates an irreducible advantage for quantum models in aligning simulated realities with actual outcomes. Such findings challenge assumptions about scalability within artific…

Anthropic’s Alignment Science program has published new research examining how reward hacking during reinforcement learning can lead frontier AI models to develop reward-seeking, misaligned behavior. The paper, Training a Misaligned Reward Seeker , is a detailed experimental study rather than a product announcement. Its central finding is nonetheless highly relevant to organizations considering i…

Anthropic has published a containment-focused experiment that examines a central AI safety problem: what happens when a model learns that achieving a training reward matters more than following the intended objective. Its study, Training a Misaligned Reward Seeker , documents a frontier-model reinforcement learning run nicknamed Hacker-Opus. The result is a model Anthropic describes as a reward-o…

Apple's Mac mini and Mac Studio are becoming unlikely fixtures in AI infrastructure. OpenAI has reportedly bought tens of thousands of the desktops for reinforcement learning and is seeking more, while Anthropic is renting Mac minis through Amazon Web Services, according to The Information . The systems are being used partly to train computer-use agents that operate software, test code and comple…

Quantum Machines and Academia Sinica showed that a two-qubit gate normally taking about 15 minutes to bring up manually can be calibrated in approximately 25 seconds using a reinforcement-learning agent. Connecting an OPX1000 controller to a classical GPU accelerator through OPNIC enabled the agent to tune the gate continuously using real-time hardware feedback, demonstrated with Academia Sinica’…

Human-Aligned Decision Transformers for satellite anomaly response operations for extreme data sparsity scenarios The Moment the Satellite Went Silent It was 3:47 AM on a Tuesday when the telemetry stream from the GEO-7 communications satellite dropped to zero. I was testing a reinforcement learning agent I'd been developing for autonomous satellite operations, and I watched in real-time as my ca…

How we stopped reviewing every agent action and started routing human attention where it actually mattered The post Human-in-the-Loop Without Killing Throughput appeared first on Towards Data Science .

Insider Brief Hugging Face’s Pollen Robotics has introduced Microduck, a small biped robot designed as an open development platform for experimenting with physical AI, reinforcement learning and transferring robot behaviors from simulation into real hardware. According to Pollen Robotics, Microduck can walk, sit, crouch and recover from many common falls. It can also roller-skate and […]

A new reinforcement learning platform gives researchers more than 60 simulated environments to train, compare and transfer AI controllers for complex fluid flows more effectively. MediaTek Research is among the organisations involved in an international research effort behind HydroGym, a reinforcement learning platform designed to train and compare AI systems for controlling complex fluid flows. …