Reinforcement Learning
[gym](https://github.com/openai/gym) [Reinforcement](https://texonom.com/reinforcement-a04826d130484e65b22afbf08ca96800) [baselines](https://github.com/openai/baselines)


### Its label is a real-valued reward signal (possibly delayed)
어떤 환경 안에서 정의된 에이전트가 현재의 상태를 인식하여, 선택 가능한 행동들 중 보상을 최대화하는 행동 혹은 행동 순서를 선택하는 방법
에이전트가 환경과 상호작용하여 데이터를 수집 혹은 human feedback
Special form of Supervised learning
- Agent
- Environment
- Action
- State
- Reward
<details><summary>

**Basic idea - **random** **action →check reward → observation given state → learn → make policy</summary>

- Receive feedback in the form of rewards
- Agent’s utility is defined by the reward function
- Must (learn to) act so as to maximize expected rewards
- All learning is based on observed samples of outcomes
</details>

### Reinforcement Learning Notion
|Title|
|:-:|
|[Markov decision process](https://texonom.com/markov-decision-process-d01c5c23d56648ce843727514008e25c)|
|[RL Exploration](https://texonom.com/rl-exploration-3b4038d0640f4fe184558e3d477cbec2)|
|[RL Exploitation](https://texonom.com/rl-exploitation-caee227c108e4139b66ec1e7f71ab80c)|
|[Reinforcement Learning Term](https://texonom.com/reinforcement-learning-term-c0e49da62c5a4690b9e6d653fc63d45a)|
|[Passive reinforcement learning](https://texonom.com/passive-reinforcement-learning-5ff410c59608455380eb53627655fc1b)|
|[Evolutionary Learning](https://texonom.com/evolutionary-learning-b63b9a0ef63845d3bfea5b9e0abb9692)|
|[Model based learning](https://texonom.com/model-based-learning-7e1b7de3d4b54e7a8281dc4e5cd6dbb4)|
|[Policy Iteration](https://texonom.com/policy-iteration-55cbe9219f8a48fbb850f64b677e847a)|
|[Bellman Expectation Equation](https://texonom.com/bellmanexpectation-equation-1135fc0eb9bb46388bb1453399d8cca6)|
|[State-value function](https://texonom.com/state-value-function-d9d3467fea994443b2336fd7958dc00b)|
|[Q Function](https://texonom.com/q-function-115a8a9d62554ebf9fbb46efb3f263ca)|



### Reinforcement Learnings
|Title|
|:-:|
|[RLHF](https://texonom.com/rlhf-4b184f9c9e8b4c7a8861fb6374e91aa6)|
|[RRHF](https://texonom.com/rrhf-073c6bdf48444a1b9acad9e65057f3d0)|
|[DQN](https://texonom.com/dqn-d3f9810ec70640a0b940b2d0c214adbb)|
|[PPO](https://texonom.com/ppo-87ce8ebe81f84ac1a7617b1f6def9e26)|
|[ACER](https://texonom.com/acer-a0eafd5e768c4f62955b12fc428e8719)|
|[TRPO](https://texonom.com/trpo-23c0f917124d419cbfe478d20b804276)|
|[A3C](https://texonom.com/a3c-eb1de7310234443d986317171949eaff)|
|[SARSA](https://texonom.com/sarsa-e5d847a4fb6e41cdad5b9ffb6e974a10)|
|[Q-Learning](https://texonom.com/q-learning-6fb81e4a53ab4e3097784cde99c8c038)|




> [What is Reinforcement Learning · Fundamental of Reinforcement Learning](https://dnddnjs.gitbooks.io/rl/content/what_is_reinforcement_learning.html)






