Category: Game AI with Reinforcement Learning
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Advanced Game AI: Multi-Agent RL and Self-Play
Build AlphaGo-style game AI: self-play, curriculum learning, and population training. Why single-agent RL fails for competitive games.
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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.