Adding::Glossary for Unit-1

.units/unit1/glossary.mdx

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+# Glossary [[glossary]]
+
+### 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.