• Definition and core concepts: agents, environments, states, actions, rewards.
• Difference between RL, supervised learning, and unsupervised learning.
• Key characteristics of RL: exploration vs. exploitation, delayed rewards.

• Real-world applications: robotics, gaming, recommendation systems, finance, and autonomous vehicles.
• Examples: AlphaGo, self-driving cars, robotics control systems.

Key Concepts in RL

• Agent: Learns and interacts with the environment.
• Environment: The external system the agent interacts with.
• State: The current situation of the agent in the environment.
• Action: Choices the agent can make.
• Reward: Feedback the agent receives after taking an action.
• Policy: A strategy that the agent follows to decide actions.
• Value Function: Estimation of the future rewards the agent will receive.

Markov Decision Process (MDP)

• Understanding the MDP framework: states, actions, transitions, and rewards.
• Formal definition and components of MDP: states (S), actions (A), reward (R), transition probabilities (T), and discount factor (γ).

Balancing Exploration and Exploitation

• Exploration: Trying new actions to discover their rewards.
• Exploitation: Choosing actions that have already been found to give high rewards.
• Strategies for balancing: epsilon-greedy, softmax, Upper Confidence Bound (UCB).

Epsilon-Greedy Algorithm

• Exploration with randomness and exploitation with the best-known action.
• Setting epsilon (ε) and decay over time to gradually shift from exploration to exploitation.

Reward Function and Return

• The concept of immediate rewards and long-term rewards (return).
• Discounting future rewards: The discount factor (γ) and its impact on the agent’s decisions.
• Understanding the discounted sum of rewards over time.

Value Function and Bellman Equation

• The value of a state: Expected long-term return starting from a given state.
• The Bellman equation for state value: V(s)=R(s)+γ∑s′P(s′∣s)V(s′)V(s) = R(s) + \gamma \sum_{s’} P(s’|s) V(s’)V(s)=R(s)+γ∑s′​P(s′∣s)V(s′).

• Overview of value iteration for solving RL problems.
• The algorithm for iteratively updating the value of each state until convergence.

• Policy evaluation and policy improvement steps.
• Alternating between improving the policy and evaluating it.

• Q-values: Action-value function, which estimates the quality of a given action in a state.
• The Q-Learning algorithm: How the agent learns the best action by interacting with the environment.
• Bellman equation for Q-values:
  Q(s,a)=R(s,a)+γmax⁡a′Q(s′,a′)Q(s, a) = R(s, a) + \gamma \max_{a’} Q(s’, a’)Q(s,a)=R(s,a)+γmaxa′​Q(s′,a′)

• Steps of Q-Learning algorithm: initialization, exploration, updating Q-values, and policy extraction.
• Example: Solving a simple grid-world problem with Q-learning.

Introduction to Deep Q-Learning

• Extending Q-Learning using deep neural networks for large state spaces.
• The concept of a deep Q-network (DQN) to approximate Q-values.
• Using experience replay and target networks to stabilize training.

Implementing Deep Q-Learning

• Practical implementation of DQN on simple environments like OpenAI Gym.
• Training a neural network to predict Q-values for a given state-action pair.

• Implement a reinforcement learning solution for a real-world problem (e.g., training a robot to perform a task, playing a game, or optimizing a recommendation system).
• The project will involve setting up the environment, defining the reward structure, and applying RL algorithms to solve the problem.