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SKU/Artículo: AMZ-B0FWY99H3G
Reinforcement Learning in Java : Build AI with MDPs, Q-Learning, and Policy Optimization
Format:
Kindle
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.97 kg
0.97 kg
Devolución:
No
No
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Struggling to bridge the gap between RL theory and actual code, watching algorithms flop in Java because tutorials skim over implementation details like reward shaping or exploration strategies? You're not alone—devs drown in math-heavy papers, battle unstable Q-tables that diverge wildly, and hit walls deploying agents that underperform in real chaos, leaving your AI dreams stalled and resumes thin. What if you could code smart agents that learn, adapt, and crush benchmarks with Java's rock-solid backbone? Power up with Reinforcement Learning in Java: Build AI with MDPs, Q-Learning, and Policy Optimization—Your Complete Hands-On Guide to Building Intelligent Agents with Practical Java Projects – Unlock the Power of RL for Real-World AI Applications. By Bit Bryson, this isn't vague academia; it's your code-forged arsenal for Reinforcement Learning in Java, packed with runnable projects that Build AI with MDPs, Q-Learning, and Policy Optimization from setup to supremacy. Crush the curve: Build AI with MDPs, Q-Learning, and Policy Optimization through deployable firepower. Reinforcement Learning in Java Foundations: Master MDPs and value iteration—step-by-step Java classes for gridworld envs that converge fast, dodging discount factor traps. Q-Learning Supercharged: Implement tabular and deep Q-networks with epsilon-greedy; hands-on tweaks for FrozenLake and CartPole that boost rewards 200%. Policy Optimization Mastery: Code REINFORCE and actor-critic agents—actionable gradients for continuous control, integrated with Java ML libs for seamless scaling. Build Intelligent Agents Pro-Style: From bandit problems to robotics sims; exercises forge Reinforcement Learning in Java portfolios with real-world tweaks like multi-agent games. Unlock RL for Real-World AI Applications: Environment wrappers, hyperparam tuning, and eval metrics—evade overfitting with targeted tests, ready for finance bots or game AIs. You're engineered for epic agents—Build AI with MDPs, Q-Learning, and Policy Optimization now. Secure Reinforcement Learning in Java today—click Buy, run your first episode, and architect the AI revolution! reinforcement learning java q-learning java tutorial mdp java implementation policy optimization java build ai agents java hands-on rl java book java reinforcement learning projects Computers & Technology > Artificial Intelligence & Machine Learning > Reinforcement Learning Computers & Technology > Programming Languages > Java Computers & Technology > Software Development > Artificial Intelligence & Machine Learning
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U$S 12,48
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