Frontiers

Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment

1 Applied Neurocognitive Systems, Fraunhofer Institute for Industrial Engineering (IAO), Stuttgart, Germany 2 Applied Neurocognitive Systems, Institute of Human Factors and Technology Management (IAT) ...
Tech Xplore on MSN

Reinforcement learning accelerates model-free training of optical AI systems

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
GitHub

Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer

*: Equal contribution. Project Co-lead., †: Corresponding Author. Humanoid-Gym is an easy-to-use reinforcement learning (RL) framework based on Nvidia Isaac Gym, designed to train locomotion skills ...
Frontiers

Decentralized multi-agent reinforcement learning based on best-response policies

Introduction: Multi-agent systems are an interdisciplinary research field that describes the concept of multiple decisive individuals interacting with a usually partially observable environment. Given ...

True agentic AI is years away - here's why and how we get there

Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.
Analytics Insight

What are the Best Python Libraries for Reinforcement Learning in 2025?

Overview:  Reinforcement learning in 2025 is more practical than ever, with Python libraries evolving to support real-world simulations, robotics, and deci ...