Deep Reinforcement Learning (DRL)

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• Reinforcement Learning (RL)
• IMPALA (Importance Weighted Actor-Learner Architecture)

OTHER: Learning; MDP, Q, and SARSA

• Markov Decision Process (MDP)
• Deep Q Learning (DQN)
• Neural Coreference
• State-Action-Reward-State-Action (SARSA)

OTHER: Policy Gradient Methods

• Policy Gradient (PG)
• Trust Region Policy Optimization (TRPO)
• Proximal Policy Optimization (PPO)

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• Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG) | Steeve Huang
• Introduction to Various Reinforcement Learning Algorithms. Part II (TRPO, PPO) | Steeve Huang
• Guide

Goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. Reinforcement learning solves the difficult problem of correlating immediate actions with the delayed returns they produce. Like humans, reinforcement learning algorithms sometimes have to wait a while to see the fruit of their decisions. They operate in a delayed return environment, where it can be difficult to understand which action leads to which outcome over many time steps.