Congratulations! You have made it to the final chapter. We have come a long way! We started off with the very basics of RL, such as MDP, Monte Carlo methods, and TD learning and moved on to advanced deep reinforcement learning algorithms such as DQN, DRQN, and A3C. We have also learned about interesting state-of-the-art policy gradient methods such as DDPG, PPO, and TRPO, and we built a car-racing agent as our final project. But RL still has a lot more for us to explore, with increasing advancements each and every day. In this chapter, we will learn about some of the advancement in RL followed by hierarchical and inverse RL.

In this chapter, you will learn the following:

• Imagination augmented agents (I2A)
• Learning from human preference
• Deep Q learning from demonstrations
• Hindsight experience replay
• Hierarchical reinforcement learning
• Inverse reinforcement learning