Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces

Understand

Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life.

• In non-structured environments and in the presence of uncertainties, such degrees of intelligence are not easy to obtain.
• Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fashion without any need for hand-crafted features or policies.
• Especially in the context of robotics, in which the cost of real-world data is usually extremely high, reinforcement learning solutions achieving high sample efficiency are needed.