Inverse dynamics models are applied to a plethora of robot control tasks such as computed-
torque control, which are essential for trajectory execution. The analytical derivation of such …

Over the past several decades, reinforcement learning has emerged as one of the major
paradigms in machine learning because it allows an agent to learn through interaction with …

To deal with uncertain environments, autonomous agents need to be able to learn without
supervision. However, reward-based interactive learning often exhibits limitations handling …

The use of reinforcement learning for robot teams has enabled complex tasks to be
performed, but at the cost of requiring a large amount of exploration. Exchanging information …

A major bottleneck for developing general reinforcement learning agents is determining
rewards that will yield desirable behaviors under various circumstances. We introduce a …