  Robotic grasping is a crucial area of research as it can result in the
acceleration of the automation of several Industries utilizing robots ranging
from manufacturing to healthcare. Reinforcement learning is the field of study
where an agent learns a policy to execute an action by exploring and exploiting
rewards from an environment. Reinforcement learning can thus be used by the
agent to learn how to execute a certain task, in our case grasping an object.
We have used the Pick and Place environment provided by OpenAI's Gym to
engineer rewards. Hindsight Experience Replay (HER) has shown promising results
with problems having a sparse reward. In the default configuration of the
OpenAI baseline and environment the reward function is calculated using the
distance between the target location and the robot end-effector. By weighting
the cost based on the distance of the end-effector from the goal in the x,y and
z-axes we were able to almost halve the learning time compared to the baselines
provided by OpenAI, an intuitive strategy that further reduced learning time.
In this project, we were also able to introduce certain user desired
trajectories in the learnt policies (city-block / Manhattan trajectories). This
helps us understand that by engineering the rewards we can tune the agent to
learn policies in a certain way even if it might not be the most optimal but is
the desired manner.
