Unit 1: Core Concepts of Reinforcement Learning
Unit 2: The RL Framework and its Place
Unit 3: Markov Decision Processes (MDPs)
Unit 4: Types of RL Problems
Deep Reinforcement Learning: DQN with TensorFlow/PyTorch for Autonomous Models
Master Deep Q-Networks (DQN) using TensorFlow/PyTorch to build intelligent, autonomous models and solve complex reinforcement learning problems.
Unit 1: Neural Network Architectures
Unit 2: Forward and Backpropagation
Unit 3: Activation Functions and Optimization
Unit 1: Understanding the Q-Function
Unit 2: The Bellman Equation
Unit 3: Q-Learning Algorithm
Unit 1: DQN: The Big Picture
Unit 2: DQN Algorithm Deep Dive
Unit 3: DQN: Challenges and Solutions
Unit 1: Understanding Experience Replay
Unit 2: Implementing Experience Replay
Unit 3: Advanced Experience Replay Techniques
Unit 1: Understanding Target Networks
Unit 2: Implementing Target Networks
Unit 3: Impact and Analysis
Unit 1: Environment Setup and Exploration
Unit 2: DQN Architecture with TensorFlow/PyTorch
Unit 3: Experience Replay and Target Networks
Unit 4: Training the DQN Agent
Unit 1: DQN Performance Metrics
Unit 2: Hyperparameter Tuning Techniques
Unit 3: Analyzing Learning Curves
Unit 4: Strategies for Improving DQN Performance
Unit 1: Double DQN: Addressing Overestimation
Unit 2: Dueling DQN: Value and Advantage
Unit 3: Prioritized Experience Replay
Unit 1: Beyond Epsilon-Greedy: Alternative Exploration Strategies
Unit 2: Intrinsic Motivation and Curiosity-Driven Exploration
Unit 3: Implementation and Evaluation
Unit 1: DQN Limitations
Unit 2: Policy Gradients
Unit 3: Actor-Critic Methods & Applications