Update glossary.mdx

units/en/unit1/glossary.mdx

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 This is a community-created glossary. Contributions are welcomed!
 
 ### 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.
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+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
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 - **State**:  Complete description of the state of the world.
 - **Observation**: Partial description of the state of the environment/world.
 
 ### Actions
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 - **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
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 - **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
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 - **Episodic**: Has a starting point and an ending point.
 - **Continuous**: Has a starting point but no ending point.
 
 ### Exploration v/s Exploitation Trade-Off
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 - **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
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 - **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:
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 - 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:
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 - 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.