  Recent advances in Reinforcement Learning (RL) have surpassed human-level
performance in many simulated environments. However, existing reinforcement
learning techniques are incapable of explicitly incorporating already known
domain-specific knowledge into the learning process. Therefore, the agents have
to explore and learn the domain knowledge independently through a trial and
error approach, which consumes both time and resources to make valid responses.
Hence, we adapt the Deep Deterministic Policy Gradient (DDPG) algorithm to
incorporate an adviser, which allows integrating domain knowledge in the form
of pre-learned policies or pre-defined relationships to enhance the agent's
learning process. Our experiments on OpenAi Gym benchmark tasks show that
integrating domain knowledge through advisers expedites the learning and
improves the policy towards better optima.
