CS702(B) Unit 4 Reinforcement Learning Fundamentals study material for RGPV CSE 7th Semester. Learn reinforcement learning, bandit algorithms, UCB, PAC, MDP, Bellman optimality, dynamic programming, value iteration, policy iteration, Q-learning and temporal difference learning.
Unit 4 introduces Reinforcement Learning, where an agent learns by interacting with an environment and receiving rewards. It covers Bandit Problems, Markov Decision Process, Bellman equations, Dynamic Programming, Value Iteration, Policy Iteration, Q-Learning and Temporal Difference methods.
Understand agent, environment, action, state, reward and policy.
Learn Markov Decision Process and Bellman optimality equations.
Study value iteration, policy iteration, Q-learning and temporal difference learning.
Complete syllabus-based topics of Deep & Reinforcement Learning Unit 4.
Reinforcement Learning is a learning method where an agent learns actions by interacting with an environment and receiving rewards.
The agent performs actions, the environment responds with new states and rewards, and the agent learns the best behavior.
Reward is feedback received after an action, while policy defines the agent’s strategy for selecting actions.
Bandit algorithms solve decision-making problems where an agent must choose among multiple actions with uncertain rewards.
UCB balances exploration and exploitation by selecting actions using confidence estimates.
PAC means Probably Approximately Correct. It provides a framework to analyze learning performance with probability guarantees.
Median Elimination is a PAC-based algorithm used to identify near-best actions in bandit settings.
Policy Gradient methods directly optimize the policy by adjusting parameters in the direction of expected reward improvement.
Full RL deals with sequential decision-making where current actions affect future states and rewards.
MDP is a mathematical framework for RL defined by states, actions, rewards, transition probabilities and discount factor.
Bellman optimality equation defines the best possible value of a state or action using recursive reward calculation.
Dynamic Programming solves RL problems by breaking them into smaller subproblems using value functions.
Value Iteration repeatedly updates value functions until the optimal value and policy are found.
Policy Iteration alternates between policy evaluation and policy improvement to find the optimal policy.
Q-Learning learns the value of taking an action in a state without requiring a model of the environment.
TD learning combines Monte Carlo learning and Dynamic Programming by updating estimates from partial experience.
Eligibility traces assign credit to recently visited states or actions to improve learning efficiency.
Function approximation estimates value functions or policies using models when the state space is large.
Least Squares methods are used to approximate value functions by minimizing prediction error.
Reinforcement Learning: Agent environment se interact karke reward ke basis par learn karta hai.
MDP: States, actions, rewards, transition probability aur discount factor ka framework.
Bellman Equation: Current value ko future rewards ke basis par recursively define karta hai.
Q-Learning: Model-free RL algorithm jo best action-value function learn karta hai.
TD Learning: Partial experience ke basis par value update karta hai.
| Topic | Expected Frequency | Importance |
|---|---|---|
| Reinforcement Learning Basics | Very High | ⭐⭐⭐⭐⭐ |
| Bandit Algorithms | High | ⭐⭐⭐⭐ |
| UCB | Medium | ⭐⭐⭐ |
| MDP | Very High | ⭐⭐⭐⭐⭐ |
| Bellman Optimality | Very High | ⭐⭐⭐⭐⭐ |
| Value Iteration | Very High | ⭐⭐⭐⭐⭐ |
| Policy Iteration | Very High | ⭐⭐⭐⭐⭐ |
| Q-Learning | Very High | ⭐⭐⭐⭐⭐ |
| Temporal Difference Learning | Very High | ⭐⭐⭐⭐⭐ |
| Function Approximation | High | ⭐⭐⭐⭐ |
Reinforcement Learning is a learning method where an agent learns by taking actions and receiving rewards.
MDP stands for Markov Decision Process. It is a mathematical model for decision-making in RL.
Bellman Optimality equation defines the optimal value of a state or action using future rewards.
Q-Learning is a model-free RL algorithm used to learn optimal action values.
TD learning updates value estimates using partial experience without waiting for final outcome.