100 Facts About Reinforcement Learning (RL)

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Here’s a list of 100 facts about reinforcement learning:

1. Reinforcement learning (RL) is a type of machine learning where agents learn to make decisions by interacting with an environment.
2. RL is based on the idea of learning through trial and error, with the agent receiving feedback in the form of rewards or penalties.
3. The goal in reinforcement learning is for the agent to learn a policy, which is a strategy that maps states to actions, to maximize cumulative rewards over time.
4. Markov Decision Processes (MDPs) are a mathematical framework used to model reinforcement learning problems.
5. States, actions, rewards, and the transition function define the components of an MDP.
6. The exploration-exploitation dilemma is a fundamental challenge in RL, balancing the exploration of new actions and the exploitation of known actions.
7. Q-learning is a popular RL algorithm that learns a state-action value function and can be used for discrete action spaces.
8. Deep Q Network (DQN) combines Q-learning with deep neural networks and is effective in handling high-dimensional state spaces.
9. Policy Gradient methods directly optimize the policy, making them suitable for continuous action spaces.