Benchmarking Reinforcement Learning Algorithms on Real-World Robots

Understand

Through many recent successes in simulation, model-free reinforcement learning has emerged as a promising approach to solving continuous control robotic tasks.

• The research community is now able to reproduce, analyze and build quickly on these results due to open source implementations of learning algorithms and simulated benchmark tasks.
• To carry forward these successes to real-world applications, it is crucial to withhold utilizing the unique advantages of simulations that do not transfer to the real world and experiment directly with physical robots.
• However, reinforcement learning research with physical robots faces substantial resistance due to the lack of benchmark tasks and supporting source code.