Mixing Human Demonstrations with Self-Exploration in Experience Replay
  for Deep Reinforcement Learning
  We investigate the effect of using human demonstration data in the replay
buffer for Deep Reinforcement Learning. We use a policy gradient method with a
modified experience replay buffer where a human demonstration experience is
sampled with a given probability. We analyze different ratios of using
demonstration data in a task where an agent attempts to reach a goal while
avoiding obstacles. Our results suggest that while the agents trained by pure
self-exploration and pure demonstration had similar success rates, the pure
demonstration model converged faster to solutions with less number of steps.
