Q-learning

Q-learning forms the basis for many advanced reinforcement learning algorithms.

Q-learning is a model-free reinforcement learning algorithm that learns to make decisions by estimating the value (quality) of a particular action in a given state. The "Q" in Q-learning stands for "quality." Q-learning is a fundamental algorithm in reinforcement learning. Here's an explanation of the key concepts:

Key components:

Q-table: Stores the estimated Q-values for each state-action pair.

States: The current position or situation of the agent in the environment.

Actions: Possible moves the agent can make in each state.

Rewards: Feedback from the environment for each action taken.

Learning rate (α): Determines how much new information overrides old information.

Discount factor (γ): Balances immediate and future rewards.

Learning Process: The agent interacts with the environment, observing states, taking actions, and receiving rewards. It updates its Q-values based on these interactions using the Q-learning update rule.

Limitations:

-Can be slow to converge for large state spaces.

-Struggles with continuous action spaces.

-May overestimate Q-values in certain situations.

Q-learning forms the basis for many advanced reinforcement learning algorithms and has been successfully applied in various domains, from game playing to robotics.

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