A number of works have been devoted to applying reinforcement learning to the power system field, many key problems remain unsolved and there is still a substantial distance from practical implementation
We have presented the Reinforcement learning-CycleGAN to address the visual simulation-to-real gap, and showed it significantly improves real world vision-based robotics with two varied grasping setups
Our Controllable Imitative Reinforcement Learning incorporates controllable imitation learning with Deep Deterministic Policy Gradient policy learning to resolve the sample inefficiency issue that is well known in reinforcement learning research
We present modular actor-critic network architectures for action and perception in which only part of the state is exposed to the gripper controller, and where object detector modules are used to localize the object in the selected camera viewpoints
In the real world experiment using synthetic images as inputs, the agent got a consistent success rate with that in simulation. These two different results show that the failure in the real world experiment with camera images was caused by the input image differences between real...