Category: RL Complete Guide
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Part 6: Beyond Simulation: Addressing the Sim-to-Real Gap
Bridging the sim-to-real gap in RL: domain randomization, system identification, transfer learning, and deployment best practices for robotics and finance.
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Part 5: Reward Engineering: How to Shape Behaviors in Financial/Robotic Tasks
Master reward function design for RL: potential-based shaping, risk-adjusted trading rewards, sparse vs dense robotics rewards, curriculum learning, and intrinsic motivation.
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Part 4: Stable Baselines3: Practical Tips for Training Robust Agents
Master production RL with Stable Baselines3: hyperparameter tuning, training best practices, callbacks, TensorBoard monitoring, and debugging techniques.
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Part 3: Policy Gradient vs. Q-Learning: Choosing the Right Agent
Deep dive into value-based vs policy gradient methods. Compare DQN, PPO, SAC with math foundations, code examples, and practical algorithm selection guidance.
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Part 2: Building Your First Custom Gym Environment using OpenAI Gymnasium
Hands-on guide to building custom RL environments with Gymnasium. Create a stock trading environment from scratch with state design, reward engineering, and vectorization.
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Part 1: The Core of RL: Markov Decision Processes (MDP) Explained
Master the mathematical foundation of RL: MDP framework, Bellman equations, value functions, and dynamic programming with Python implementations.