Reinforcement Learning – Page 7

Exploration vs Exploitation: ε-greedy부터 UCB, Thompson까지

2025년 12월 29일

Compare ε-greedy, UCB, Thompson Sampling, and RND for sparse rewards. See which exploration strategy actually works in hard environments.

2025년 12월 26일

Dyna-Q to MuZero: how model-based RL learns environment dynamics. MCTS planning and MBPO/Dreamer implementation strategies with code.

2025년 12월 22일

Policy Gradient from scratch: REINFORCE to PPO with PyTorch code. Reduce variance 100x using baselines and see why PPO beats vanilla PG.

2025년 12월 20일

Reward shaping accelerates RL training 10x—but breaks policies if done wrong. Use potential-based shaping and avoid these 3 fatal mistakes.

2025년 12월 14일

A2C vs PPO vs SAC: real training curves from 5 environments. Hyperparameter tuning secrets that cut training time 40% with Stable-Baselines3.

2025년 12월 11일

Dreamer v3 and MBPO boost RL sample efficiency 10×. World Models learn environment dynamics—RSSM architecture and ensemble tricks explained.

2025년 12월 03일

Sim-to-Real transfer: train robot policies 100x faster in simulation, deploy to hardware with 85% success rate. Domain randomization code.

2025년 11월 27일

Build AlphaGo-style game AI: self-play, curriculum learning, and population training. Why single-agent RL fails for competitive games.