Category: Reinforcement Learning
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PPO Hyperparameters That Crash in Production: 5 Silent Failures
Fixed clip range, constant entropy, and training-optimized batch sizes silently kill production RL agents. Here's what actually breaks.
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SAC: The Best Algorithm for Continuous Control
Implement Soft Actor-Critic from scratch โ maximum entropy RL, twin Q-networks, automatic temperature tuning, and when to choose SAC over PPO.
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PPO: Why It Powers ChatGPT and Game AI
Deep dive into Proximal Policy Optimization โ the clipping trick, GAE, hyperparameters, and why PPO became the backbone of RLHF in modern LLMs.
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Policy Gradient and Actor-Critic Explained
Master policy gradient methods from REINFORCE to Actor-Critic with GAE. Learn why direct policy optimization unlocks continuous action spaces.
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From Q-Learning to DQN: Your First RL Algorithms
Implement Q-Learning from scratch, then scale up to Deep Q-Networks with experience replay and target networks. Complete PyTorch code included.
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RL Fundamentals: MDP, Bellman Equation, and Value Functions
Learn the foundations of Reinforcement Learning โ MDP framework, policies, value functions, Bellman equation, and exploration strategies with hands-on Python code.
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GRPO vs PPO for LLM Reasoning: DeepSeek-R1 Explained
How DeepSeek-R1 trains chain-of-thought reasoning with GRPO instead of PPO. Group Relative Policy Optimization removes the critic network, cuts memory by 40%, and produces emergent self-verification behaviors. Full algorithm breakdown with PyTorch code.
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RL Transfer Learning: Atari to Custom Tasks with SB3
Transfer a pretrained DQN from Atari Pong to custom tasks in 100 episodes. Replay buffer tricks and exploration tuning that actually work.