Reinforcement Learning in Robotics

Reinforcement Learning in Robotics is a research topic within Artificial Intelligence. Science Explorer counts 21k research works in it since 1956. 9.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers encompasses a wide range of advancements in reinforcement learning algorithms and their applications, including deep learning, neural networks, robotics, autonomous control, policy gradient methods, multi-agent systems, model-based learning, curiosity-driven exploration, and simulation to real-world transfer.

• Reinforcement Learning
• Deep Learning
• Neural Networks
• Robotics
• Autonomous Control
• Policy Gradient
• Multi-Agent Systems
• Model-Based Learning
• Curiosity-Driven Exploration
• Simulation to Real-world Transfer