강화학습 – TildAlice

Model-Free vs Model-Based RL: 샘플 효율성 비교 가이드

2025년 12월 30일

Model-Free vs Model-Based RL: which needs fewer samples? Atari/MuJoCo benchmarks reveal the trade-offs you need to know before coding.

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 15일

NeurIPS 2025 Best Paper candidate achieves 3-4x faster deep RL training with Actor-Learner architecture and Adaptive V-trace. Implementation guide.

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.