Reinforcement Learning та навчання з підкріпленням

🎓 Basic Concepts

MDP Components: Markov Decision Process – a mathematical framework for modeling sequential decision-making problems.

States: The current state of the environment.

Actions: The actions that an agent can perform.

Rewards: The reward received for performing an action.

Policy: The strategy used to select actions.

Value Functions: Estimate the expected cumulative reward from a given state or state-action pair.

V(s): State value – the expected return from a state.

Q(s,a): Action value – the expected return from taking action 'a' in state 's'.

Goal: To find an optimal policy.

Exploration vs Exploitation: The balance between trying new actions and using existing knowledge to maximize reward.

🔧 Algorithms

Q-Learning: A model-free reinforcement learning algorithm that learns the optimal Q-function.

Concept: Learning the Q-function through the Bellman equation.

Formula: Q(s,a) ← Q(s,a) + α[r + γ max Q(s',a') - Q(s,a)].

Advantages: Off-policy, simple.

Policy Gradient: A method for learning policies by maximizing the expected reward.

Methods: REINFORCE, Actor-Critic, PPO.

Advantages: Suitable for continuous actions and complex policies.

Deep Q-Network (DQN): A Q-Learning algorithm using a deep neural network to approximate the Q-function.

Techniques: Experience replay, target network.

Advantages: Effective for large state spaces.

📚 Practical Examples

Example 1: Q-Learning for Grid World: Create a grid world environment. Initialize a Q-table. Train the agent using Q-Learning. Use the learned policy for navigation.

Example 2: DQN for Atari: Prepare an Atari environment. Train a DQN with experience replay. Evaluate performance on test episodes.