  In this paper, we try to improve exploration in Blackbox methods,
particularly Evolution strategies (ES), when applied to Reinforcement Learning
(RL) problems where intermediate waypoints/subgoals are available. Since
Evolutionary strategies are highly parallelizable, instead of extracting just a
scalar cumulative reward, we use the state-action pairs from the trajectories
obtained during rollouts/evaluations, to learn the dynamics of the agent. The
learnt dynamics are then used in the optimization procedure to speed-up
training. Lastly, we show how our proposed approach is universally applicable
by presenting results from experiments conducted on Carla driving and UR5
robotic arm simulators.
