標(biāo)題: Titlebook: Deep Reinforcement Learning with Python; RLHF for Chatbots an Nimish Sanghi Book 2024Latest edition Nimish Sanghi 2024 Artificial Intellige [打印本頁] 作者: 帳簿 時(shí)間: 2025-3-21 17:33
書目名稱Deep Reinforcement Learning with Python影響因子(影響力)
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書目名稱Deep Reinforcement Learning with Python網(wǎng)絡(luò)公開度
書目名稱Deep Reinforcement Learning with Python網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Reinforcement Learning with Python被引頻次
書目名稱Deep Reinforcement Learning with Python被引頻次學(xué)科排名
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書目名稱Deep Reinforcement Learning with Python讀者反饋
書目名稱Deep Reinforcement Learning with Python讀者反饋學(xué)科排名
作者: 分開如此和諧 時(shí)間: 2025-3-21 23:08
The Foundation: Markov Decision Processes,s under the branch of probability that models sequential decision-making behavior. Although most of the problems you‘ll study in reinforcement learning are modeled as . (MDP), this chapter starts by introducing Markov chains (MC) followed by Markov reward processes (MRP). Next, the chapter discusses作者: scoliosis 時(shí)間: 2025-3-22 03:16
Model-Based Approaches,nt transitions from one state to another. Equations . and . clearly indicate that .(.) and .(., .) depend on two components, the transition dynamics and the next state/state-action values. To lay the foundations of RL learning, this chapter starts with the simplest setup—one in which the transition 作者: nautical 時(shí)間: 2025-3-22 07:58 作者: CYN 時(shí)間: 2025-3-22 11:09 作者: Myocyte 時(shí)間: 2025-3-22 13:11 作者: Myocyte 時(shí)間: 2025-3-22 17:15
Improvements to DQN**, NoisyNets DQN, C-51 (Categorical 51-Atom DQN), Quantile Regression DQN, and Hindsight Experience Replay. All the examples in this chapter are coded using PyTorch. This is an optional chapter with each variant of DQN as a standalone topic. You can skip this chapter in the first pass and come back to作者: 四指套 時(shí)間: 2025-3-22 23:19 作者: 礦石 時(shí)間: 2025-3-23 01:57
Combining Policy Gradient and Q-Learning,s. You looked at policy gradients in Chapter .. Neural network training requires multiple iterations, and Q-learning, an off-policy approach, enables you to reuse sample transitions multiple times, giving you sample efficiency. However, Q-learning can be unstable at times. Further, it is an indirect作者: 褪色 時(shí)間: 2025-3-23 07:37 作者: Explicate 時(shí)間: 2025-3-23 10:28
Proximal Policy Optimization (PPO) and RLHF,er Large Language Model (LLM) and found it amazing how these models seem to follow your prompts and complete a task that you describe in English? Apart from the machinery of generative AI and transformers-driven architecture, RL also plays a very important role. Proximal Policy Optimization (PPO) us作者: Aviary 時(shí)間: 2025-3-23 14:04 作者: 小樣他閑聊 時(shí)間: 2025-3-23 18:25
Additional Topics and Recent Advances,eptual level with links to the relevant research/academic papers, where applicable. You may use these references to extend your knowledge horizon based on your individual interest area in the field of RL. Unlike previous chapters, you will not always find the detailed pseudocode or actual code imple作者: 吼叫 時(shí)間: 2025-3-24 01:37 作者: 裙帶關(guān)系 時(shí)間: 2025-3-24 06:15
guage Models using RLHF with complete code examples.Every co.Gain a theoretical understanding to the most popular libraries in deep reinforcement learning (deep RL).? This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and repli作者: Barter 時(shí)間: 2025-3-24 07:40 作者: happiness 時(shí)間: 2025-3-24 13:17 作者: 殺蟲劑 時(shí)間: 2025-3-24 18:02 作者: DUCE 時(shí)間: 2025-3-24 20:47
n in a given state. These two steps are carried out in a loop until no further improvement in values is observed. In this chapter, you look at a different approach for learning optimal policies, by directly operating in the policy space. You will learn to improve the policies without explicitly learning or using state or state-action values.作者: Abrupt 時(shí)間: 2025-3-25 00:15
Introduction to Reinforcement Learning,ans do. Recently, deep reinforcement learning has been applied to Large Language Models like ChatGPT and others to make them follow human instructions and produce output that‘s favored by humans. This is known as . (RLHF).作者: Glaci冰 時(shí)間: 2025-3-25 04:20 作者: ANTH 時(shí)間: 2025-3-25 08:01 作者: LUMEN 時(shí)間: 2025-3-25 12:41 作者: 極深 時(shí)間: 2025-3-25 19:16 作者: chastise 時(shí)間: 2025-3-25 20:59 作者: laxative 時(shí)間: 2025-3-26 00:36
,Führung in der ?ffentlichen Verwaltung,that has a good theoretical foundation and then with a nonlinear approach with neural networks. This aspect of combining deep learning with reinforcement learning is the most exciting development and has moved reinforcement learning algorithms to scale.作者: modish 時(shí)間: 2025-3-26 07:22
ears is still the state-of-the-art policy-based optimization technique in RL. This is followed by a quick overview of LLMs—the architecture, the training process, and the overall LLM ecosystem. The chapter walks through a complete demo of RLHF tuning on a LLM using the state-of-the-art approaches.作者: 宣稱 時(shí)間: 2025-3-26 12:09 作者: 恫嚇 時(shí)間: 2025-3-26 13:01
Proximal Policy Optimization (PPO) and RLHF,ears is still the state-of-the-art policy-based optimization technique in RL. This is followed by a quick overview of LLMs—the architecture, the training process, and the overall LLM ecosystem. The chapter walks through a complete demo of RLHF tuning on a LLM using the state-of-the-art approaches.作者: Priapism 時(shí)間: 2025-3-26 20:50 作者: 樂器演奏者 時(shí)間: 2025-3-26 21:51 作者: 聯(lián)想記憶 時(shí)間: 2025-3-27 03:46 作者: 善于騙人 時(shí)間: 2025-3-27 07:13
Frauen in Führungspositionen – Einige Faktens under the branch of probability that models sequential decision-making behavior. Although most of the problems you‘ll study in reinforcement learning are modeled as . (MDP), this chapter starts by introducing Markov chains (MC) followed by Markov reward processes (MRP). Next, the chapter discusses作者: Hyperplasia 時(shí)間: 2025-3-27 09:35
Karl-Heinz Fittkau,Jakob Müller,Nicole Juffant transitions from one state to another. Equations . and . clearly indicate that .(.) and .(., .) depend on two components, the transition dynamics and the next state/state-action values. To lay the foundations of RL learning, this chapter starts with the simplest setup—one in which the transition 作者: 嘮叨 時(shí)間: 2025-3-27 15:50 作者: 遺傳 時(shí)間: 2025-3-27 20:05
,Führung in der ?ffentlichen Verwaltung,ch (MC), and finally at the temporal difference (TD) approach. In all these approaches, you saw problems where the state space and actions were discrete. Only in the previous chapter, toward the end, did I talk about Q-learning in a continuous state space. You discretized the state values using an a作者: strain 時(shí)間: 2025-3-28 00:04 作者: CRATE 時(shí)間: 2025-3-28 03:34 作者: Generalize 時(shí)間: 2025-3-28 07:45 作者: lattice 時(shí)間: 2025-3-28 12:38 作者: Mindfulness 時(shí)間: 2025-3-28 15:38
Kundenfokussierung nach ethischen Standards,ent approaches. Specifically, the chapter combines the Model-based approaches and Model-free approaches to make the algorithms more powerful and sample efficient. This approach leverages the best of both of them and is the main emphasis of this chapter. You will also study the Exploration-exploitati作者: SMART 時(shí)間: 2025-3-28 22:26
er Large Language Model (LLM) and found it amazing how these models seem to follow your prompts and complete a task that you describe in English? Apart from the machinery of generative AI and transformers-driven architecture, RL also plays a very important role. Proximal Policy Optimization (PPO) us作者: concentrate 時(shí)間: 2025-3-28 22:54
HF fine-tuning. You may have noticed that the focus has always been on only one agent in the environment that learns to act optimally using RL training algorithms. However, there is a whole range of settings with more than one agent. These agents in the environment—either individually or in a collab作者: 粗魯性質(zhì) 時(shí)間: 2025-3-29 06:36 作者: 宿醉 時(shí)間: 2025-3-29 07:23 作者: 動(dòng)機(jī) 時(shí)間: 2025-3-29 11:51
Nimish SanghiExplains deep reinforcement learning implementation using TensorFlow, PyTorch and OpenAI Gym.Comprehensive coverage on fine-tuning Large Language Models using RLHF with complete code examples.Every co作者: cortex 時(shí)間: 2025-3-29 17:22 作者: 憲法沒有 時(shí)間: 2025-3-29 20:55
Book 2024Latest editions such as Hugging Face Hub. The code is in Jupyter Notebook, which canbe run on Google Colab, and other similar deep learning cloud platforms, allowing you to tailor the code to your own needs.?.Whether it’s for applications in gaming, robotics, or Generative AI,?.Deep Reinforcement Learning with Py作者: Awning 時(shí)間: 2025-3-30 01:25 作者: 憤世嫉俗者 時(shí)間: 2025-3-30 04:11 作者: insular 時(shí)間: 2025-3-30 08:13
Deep Q-Learning (DQN),he field of robotics. However, be aware that these are still research environments useful to gain more insights. As some of these have multi-dimensional continuous value actions, there are better algorithms than DQN for training agents. This chapter focuses on learning about these environments and h作者: amplitude 時(shí)間: 2025-3-30 16:19 作者: CRAFT 時(shí)間: 2025-3-30 17:24 作者: locus-ceruleus 時(shí)間: 2025-3-30 22:56
ab, and other similar deep learning cloud platforms, allowing you to tailor the code to your own needs.?.Whether it’s for applications in gaming, robotics, or Generative AI,?.Deep Reinforcement Learning with Py979-8-8688-0272-0979-8-8688-0273-7作者: dendrites 時(shí)間: 2025-3-31 03:52
