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Dexterous multi-fingered robotic hands can perform a wide range ofmanipulation skills, making them an appealing component for general-purposerobotic manipulators. However, such hands pose a major challenge for autonomouscontrol, due to the high dimensionality of their configuration space andcomplex intermittent contact interactions. In this work, we propose deepreinforcement learning (deep RL) as a scalable solution for learning complex,contact rich behaviors with multi-fingered hands. Deep RL provides anend-to-end approach to directly map sensor readings to actions, without theneed for task specific models or policy classes. We show that contact-richmanipulation behavior with multi-fingered hands can be learned by directlytraining with model-free deep RL algorithms in the real world, with minimaladditional assumption and without the aid of simulation. We learn a variety ofcomplex behaviors on two different low-cost hardware platforms. We show thateach task can be learned entirely from scratch, and further study how thelearning process can be further accelerated by using a small number of humandemonstrations to bootstrap learning. Our experiments demonstrate that complexmulti-fingered manipulation skills can be learned in the real world in about4-7 hours for most tasks, and that demonstrations can decrease this to 2-3hours, indicating that direct deep RL training in the real world is a viableand practical alternative to simulation and model-based control.\url{https://sites.google.com/view/deeprl-handmanipulation}

Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost