Category: Reinforcement Learning
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High Discount Factor ฮณ=0.99 Causes Divergence: Fix Guide
Fix high discount factor ฮณ=0.99 divergence in RL with gradient clipping, reward scaling, and target networks. Practical debugging solutions.
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RLHF vs SFT: Why Supervised Fine-Tuning Wins 60% of Time
RLHF burned $50K before we admitted supervised fine-tuning worked better. Real cost, speed, and performance data from production LLM deployments.
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Gymnasium render_mode=’human’ Crashes Training: 3 Fixes
Gymnasium render_mode='human' crashes your RL training? Discover 3 proven fixes for headless servers and stable visualization workflows.
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DDPG from Scratch: 400-Line PyTorch Implementation
Build a DDPG agent from scratch in 400 lines of PyTorch. Learn continuous action RL with policy gradients, replay buffers, and target networks.
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Off-Policy RL Replay Buffer Memory Leak: Fix 2M Step Crash
Fix off-policy RL replay buffer memory leak causing 2M step crashes. Learn circular buffer implementation and memory-efficient sampling patterns.
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CleanRL vs Stable Baselines3: PPO Training 2.3x Faster
Compare CleanRL vs Stable Baselines3 PPO implementations and discover why CleanRL achieves 2.3x faster training with cleaner, hackable code.
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PPO vs SAC: 1-GPU Memory & Compute Cost Benchmark
SAC uses 40% more VRAM than PPO on the same taskโbut reaches target rewards 34% faster. Real benchmarks on RTX 3090 with memory and compute trade-offs.
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Stable Baselines3 VecEnv Reset Bug: 100K Step Desync Fix
Fix the Stable Baselines3 VecEnv reset bug causing 100K step desyncs. Learn why auto_reset breaks training and how to solve it properly.