# Glossary [[glossary]]

This is a community-created glossary. Contributions are welcomed!

### Agent

An agent learns to **make decisions by trial and error, with rewards and punishments from the surroundings**.

### Environment

An environment is a simulated world **where an agent can learn by interacting with it**.

### Markov Property

It implies that the action taken by our agent is **conditional solely on the present state and independent of the past states and actions**.

### Observations/State

- **State**:  Complete description of the state of the world.
- **Observation**: Partial description of the state of the environment/world.

### Actions

- **Discrete Actions**: Finite number of actions, such as left, right, up, and down.
- **Continuous Actions**: Infinite possibility of actions; for example, in the case of self-driving cars, the driving scenario has an infinite possibility of actions occurring.

### Rewards and Discounting

- **Rewards**: Fundamental factor in RL. Tells the agent whether the action taken is good/bad.
- RL algorithms are focused on maximizing the **cumulative reward**.
- **Reward Hypothesis**: RL problems can be formulated as a maximisation of (cumulative) return.
- **Discounting** is performed because rewards obtained at the start are more likely to happen as they are more predictable than long-term rewards.

### Tasks

- **Episodic**: Has a starting point and an ending point.
- **Continuous**: Has a starting point but no ending point.

### Exploration v/s Exploitation Trade-Off

- **Exploration**: It's all about exploring the environment by trying random actions and receiving feedback/returns/rewards from the environment.
- **Exploitation**: It's about exploiting what we know about the environment to gain maximum rewards.
- **Exploration-Exploitation Trade-Off**: It balances how much we want to **explore** the environment and how much we want to **exploit** what we know about the environment.

### Policy

- **Policy**: It is called the agent's brain. It tells us what action to take, given the state.
- **Optimal Policy**: Policy that **maximizes** the **expected return** when an agent acts according to it. It is learned through *training*.

### Policy-based Methods:

- An approach to solving RL problems.
- In this method, the Policy is learned directly. 
- Will map each state to the best corresponding action at that state. Or a probability distribution over the set of possible actions at that state.

### Value-based Methods:

- Another approach to solving RL problems.
- Here, instead of training a policy, we train a **value function** that maps each state to the expected value of being in that state.

Contributions are welcomed 🤗

If you want to improve the course, you can [open a Pull Request.](https://github.com/huggingface/deep-rl-class/pulls)

This glossary was made possible thanks to:

- [@lucifermorningstar1305](https://github.com/lucifermorningstar1305)
- [@daspartho](https://github.com/daspartho)
- [@misza222](https://github.com/misza222)

