Category: Reinforcement Learning
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RLHF in 2026: Why Human Feedback Still Beats Pure AI Alignment
Explore RLHF in 2026 and discover why human feedback remains essential for AI alignmentโeven as models grow more capable. The surprising reasons inside.
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Gymnasium Custom Env Step() Returns Invalid Shape: 5 Fixes
Fix Gymnasium custom env step() shape errors with 5 proven solutions. Learn proper observation space handling and avoid common pitfalls.
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PPO vs SAC Sparse Rewards: 3x Sample Efficiency Gap
PPO vs SAC on sparse rewards: which RL algorithm learns faster? Benchmark shows 3x sample efficiency gap. Compare training curves and understand why.
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DQN vs Rainbow: 4.8x Score Gain From 6 Extensions
Compare DQN and Rainbow's 6 RL extensions that achieve 4.8x higher Atari scores. See how prioritized replay and dueling nets stack up.
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DQN vs Double DQN vs Dueling DQN: Atari Breakout Benchmark
Compare DQN, Double DQN, and Dueling DQN performance on Atari Breakout. Which architecture solves overestimation and learns faster? Benchmark results inside.
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PPO Entropy Decay Bug: Why Exploration Dies at 500K Steps
Your PPO agent flatlines at 500K steps because entropy coefficient decay silently kills exploration. Here's the adaptive fix that saved my Ant-v4 runs.
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Q-Learning from Scratch: 50-Line Agent Beats Random by 94%
Write a 50-line Q-Learning agent that beats random policy by 94% on FrozenLake. Hyperparameter mistakes, convergence curves, and why it fails on CartPole.
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On-Policy vs Off-Policy RL: PPO vs SAC on 5 Gymnasium Tasks
Compare PPO and SAC on 5 Gymnasium tasks. Discover which RL algorithm wins in sample efficiency, stability, and performance across environments.