Category: Reinforcement Learning
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PPO and A3C for Complex Game AI Environments
Why PPO replaced DQN for game AI: actor-critic variance reduction, continuous control, and 10x faster convergence on Atari benchmarks.
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Deep Q-Networks (DQN): Training AI to Play Atari Games
Build a Deep Q-Network that beats human Atari scores. Experience replay, target networks, and Double DQN explained with training code.
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Q-Learning for Grid Worlds: Building Your First Game AI Agent
Train your first game AI in 50 lines of Python โ no neural networks needed. Pure Q-learning with the Bellman equation for grid worlds.
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Game AI with RL: Why It Beats Traditional Methods
Why RL beats rule-based game AI: exploration-exploitation tradeoff, credit assignment, and when traditional pathfinding still wins.
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MARL ์ค์ ๊ฐ์ด๋: QMIX, MAPPO, MADDPG ๊ตฌํ ๋น๊ต
QMIX vs MAPPO vs MADDPG on cooperative tasks: which multi-agent RL algorithm converges faster? Benchmarks from 10M training steps.
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Offline RL ์ ๋ณต: CQL, IQL, Decision Transformer ๋น๊ต
Offline RL showdown: CQL vs IQL vs Decision Transformer on D4RL benchmarks. Train agents without environment interaction.
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RLHF๋ก ์ธ์ด๋ชจ๋ธ ์ ๋ ฌํ๊ธฐ: ChatGPT๋ถํฐ Claude๊น์ง์ ์ค์ ๊ตฌํ ๊ฐ์ด๋
RLHF implementation guide: train ChatGPT-style models with human feedback. PPO + reward model code walkthrough included.