The book "Reinforcement Learning" by Barto and Sutton is standard literature and was study material in at least two of my lectures. The presented algorithms are quite basic, giving you a proper foundation before you can delve into deep RL.
As soon as you have got solid understanding of the basics, here are good algorithms/papers about deep RL which I'd recommend reading in the order:
DDPG / CONTINUOUS CONTROL WITH DEEP REINFORCEMENT
LEARNING by Lilicrap, Hunt et al.
PPO / Proximal Policy Optimization Algorithms by Schulman et al.
World Models / World Models by Ha and Schmidhuber
Dreamer / Dream to Control: Learning Behaviors by Latent Imagination by Hafner et al.
Note these papers are just a small part of many different approaches, but should give you a rough overview about what has been developed in the last years.