Reinforcement Learning with Python

Categories: Python

About Course

Learn how machines make decisions by interacting with environments. This course introduces concepts like agents, policies, rewards, and environments using Python and OpenAI Gym.

Course Content

Basics of Reinforcement Learning

Lesson 1: What is Reinforcement Learning?
Lesson 2: Real-World RL Applications
Lesson 3: Agent-Environment Interaction
Lesson 4: Rewards and States
Lesson 5: Markov Decision Processes
Lesson 6: Exploration vs Exploitation
Lesson 7: Policies and Value Functions
Lesson 8: Temporal Difference Learning
Lesson 9: Monte Carlo Methods
Lesson 10: Implementing Basic RL in Python

Dynamic Programming

Lesson 1: Bellman Equation
Lesson 2: Policy Evaluation
Lesson 3: Policy Iteration
Lesson 4: Value Iteration
Lesson 5: Gridworld Example
Lesson 6: Comparing DP Methods
Lesson 7: Implementing Policy Iteration
Lesson 8: Implementing Value Iteration
Lesson 9: Convergence Analysis
Lesson 10: DP Project

Value-Based Learning

Lesson 1: Introduction to Q-Learning
Lesson 2: Implementing Q-Tables
Lesson 3: SARSA Algorithm
Lesson 4: Double Q-Learning
Lesson 5: Deep Q-Networks (DQN)
Lesson 6: Experience Replay
Lesson 7: Target Networks
Lesson 8: Epsilon-Greedy Strategy
Lesson 9: Training RL Agents
Lesson 10: Game AI Project

Policy-Based Learning

Lesson 1: Introduction to Policy Gradients
Lesson 2: REINFORCE Algorithm
Lesson 3: Actor-Critic Models
Lesson 4: Advantage Actor-Critic (A2C)
Lesson 5: Deep Deterministic Policy Gradient (DDPG)
Lesson 6: Proximal Policy Optimization (PPO)
Lesson 7: Soft Actor-Critic (SAC)
Lesson 8: Continuous Action Spaces
Lesson 9: Implementing PPO in TensorFlow
Lesson 10: RL Project with PPO

Advanced RL Applications

Lesson 1: Multi-Agent Systems
Lesson 2: Hierarchical RL
Lesson 3: Imitation Learning
Lesson 4: Curriculum Learning
Lesson 5: Model-Based RL
Lesson 6: Meta Reinforcement Learning
Lesson 7: Robotics with RL
Lesson 8: Autonomous Vehicles
Lesson 9: Trading Bots with RL
Lesson 10: Capstone RL Project