Reinforcement Learning (RL)
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- Markov Decision Process (MDP)
- Monte Carlo (MC) Method - Model Free Reinforcement Learning
- Deep Reinforcement Learning (DRL) - DeepRL
- Distributed Deep Reinforcement Learning (DeepRL)
- Deep Q Learning (DQN)
- Neural Coreference
- State-Action-Reward-State-Action (SARSA)
- Deep Deterministic Policy Gradient (DDPG)
- Trust Region Policy Optimization (TRPO)
- Proximal Policy Optimization (PPO)
- AdaNet
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- Apprenticeship Learning - Inverse Reinforcement Learning (IRL)
- Lifelong Learning
- Dopamine Google DeepMind
- Inside Out - Curious Optimistic Reasoning
- World Models
- Google DeepMind AlphaGo Zero
- Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more | Maxim Lapan
- Reinforcement-Learning-Notebooks - A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python
This is a bit similar to the traditional type of data analysis; the algorithm discovers through trial and error and decides which action results in greater rewards. Three major components can be identified in reinforcement learning functionality: the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent can do. Reinforcement learning occurs when the agent chooses actions that maximize the expected reward over a given time. This is best achieved when the agent has a good policy to follow. Machine Learning: What it is and Why it Matters | Priyadharshini @ simplilearn
