Abstract
This lecture overviews Deep Reinforcement Learning that has many applications in, e.g., Game playing agents, Self-driving vehicles, Robotics (Robot cleaners) and Stock exchange agents. It covers the following topics in detail: Finite Markov Decision Processes. Elements of RL (actions, states, Policy, Reward, Value function, Q-function). RL algorithms for finding the optimal policy: Dynamic Programming, Monte Carlo, Temporal-difference learning, SARSA, Q-learning. Deep RL algorithms, DQN and its extensions, Rainbow. Policy Gradient methods. Actor Critic Methods. Imitation Learning. A Maze example is also presented.
RL states and actions.
Environment-action interaction.
Deep Reinforcement Learning v3.6.2 - Summary