About the more close to the "optimal" workspace configuration the easier for the robot to learn.

[celestialdr4g0n]

Factory Task Discussion
I've been looking at the factory task code and noticed that the reset function uses inverse kinematics to place the robot's end effector close to the target object. This is a great way to provide a strong starting point for the agent, but it might not be a robust solution for more generalized scenarios.

Improving Exploration for Object Lifting
I've been experimenting with the object lifting task, and it seems the current setup is sensitive to the robot's initial configuration. When I set the robot's end effector to start far away from the object, the learning process consistently fails. This suggests that the current exploration strategy isn't effective enough to discover the optimal path from a "far" starting point.

To address this, what algorithm parameters can we tweak to improve RL agent exploration efficiency? Or I have to adjust the existing reward functions for more general solution?