Reinforcement Learning: Key Algorithms & Strategies
"Explore the concept of Reinforcement Learning where agents learn decision-making by interacting with the environment, aiming for long-term rewards. Dive into key algorithms like Q-Learning and Deep Q Networks to understand Model-Free vs. Model-Based RL approaches."
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Dr. SNS RAJALAKSHMI COLLEGE OF ARTS & SCIENCE (Autonomous) Coimbatore -641049 DEPARTMENT OF COMPUTER APPLICATIONS(PG) COURSE NAME : 22UDA804 - Basics of Machine Learning II CS DA /II SEMESTER Unit 1 Topic 1 : reinforcement 3/15/2024 Software Process Improvement
reinforcement Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties. The goal is to find an optimal strategy (policy) that maximizes long-term rewards. grouping and grading
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reinforcement 2. Types of Reinforcement Learning . A. Model-Free vs. Model-Based RL Model-Free RL: The agent learns purely by trial and error (e.g., Q-Learning). Model-Based RL: The agent builds a model of the environment and plans ahead (e.g., AlphaZero). grouping and grading
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reinforcement . . 3. Key RL Algorithms A. Q-Learning (Value-Based RL) Uses a Q-table to store action values and updates it using the Bellman Equation: Q(s,a) Q(s,a)+ [R+ maxQ(s ,a ) Q(s,a)]Q(s, a) \leftarrow Q(s, a) + \alpha [R + \gamma \max Q(s', a') - Q(s, a)]Q(s,a) Q(s,a)+ [R+ maxQ(s ,a ) Q(s,a)] Example: Teaching an AI to play tic-tac-toe. B. Deep Q Networks (DQN) Uses neural networks instead of Q-tables for complex problems (e.g., playing Atari games). Breakthrough: DeepMind s DQN beat human players in Atari games. grouping and grading
