توضیحات
ارسال کتاب ها در بازه زمانی 8 الی 12 روز کاری انجام میشود.
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https://www.amazon.com/Deep-Reinforcement-Learning-Hands-optimization/dp/1838826998/ref=pd_aw_vtp_h_pd_aw_vtp_h_sccl_2/143-4951950-6235162?pd_rd_w=BGkqP&content-id=amzn1.sym.a42f40ae-d7c1-422d-a543-079cd2ac46c5&pf_rd_p=a42f40ae-d7c1-422d-a543-079cd2ac46c5&pf_rd_r=SKSNYZ8KWY7BC4E55QX7&pd_rd_wg=ez0Rz&pd_rd_r=57156801-fba5-4c86-9514-24b824951b02&pd_rd_i=1838826998&psc=1
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What’s new in this second edition of Deep Reinforcement Learning Hands-On?
This edition is devoted to the very latest reinforcement learning tools and techniques, focusing on new innovations in this emerging field. It includes six new chapters that give readers the hands-on ability to code intelligent learning agents to perform a range of practical tasks.
Topics include using the TextWorld environment from Microsoft Research to solve text-based interactive fiction games, solving discrete optimization problems (showcased using the Rubik’s Cube), and learning to apply RL methods to the robotics domain.
The book then dives into an important aspect of RL methods: advanced exploration. Several modern exploration techniques are described, implemented, and compared. Readers will also discover basic methods applied to a multi-agent environment.
Finally, all examples have been updated for PyTorch 1.3. PyTorch Ignite has also been introduced to make the coding more concise.
