Before revision:  Our results show that RD2 is able to achieve better performance than Ape-X DDPG and PPO with LSTM on partially observable tasks with varying complexity. We also show that the trained models adapt well to different initial states and different types of noise injected in the simulated environment .
After revision:  Our results show that RD2 is able to achieve better performance than Ape-X DDPG and PPO with LSTM . We have successfully evaluated our robot-agnostic policies on three robotic arms, Kuka KR60, Franka Panda, and UR10, in simulation .