標(biāo)題: Titlebook: Deep Reinforcement Learning with Python; With PyTorch, Tensor Nimish Sanghi Book 20211st edition Nimish Sanghi 2021 Artificial Intelligence [打印本頁] 作者: 手或腳 時(shí)間: 2025-3-21 19:03
書目名稱Deep Reinforcement Learning with Python影響因子(影響力)
書目名稱Deep Reinforcement Learning with Python影響因子(影響力)學(xué)科排名
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
書目名稱Deep Reinforcement Learning with Python年度引用
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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: 心神不寧 時(shí)間: 2025-3-21 21:57
http://image.papertrans.cn/d/image/264660.jpg作者: antiquated 時(shí)間: 2025-3-22 02:48
https://doi.org/10.1007/978-1-4842-6809-4Artificial Intelligence; Deep Reinforcement Learning; PyTorch; Neural Networks; Robotics; Autonomous Vehi作者: Commodious 時(shí)間: 2025-3-22 04:45
Implementing Continuous Integrationas led to many significant advances that are increasingly getting machines closer to acting the way humans do. In this book, we will start with the basics and finish up with mastering some of the most recent developments in the field. There will be a good mix of theory (with minimal mathematics) and作者: BILIO 時(shí)間: 2025-3-22 11:21 作者: 水獺 時(shí)間: 2025-3-22 12:53
Marc Joseph Saugey Restoration,earns a policy π(.| .) that maps states to actions. The agent uses this policy to take an action .?=?. when in state .?=?.. The system transitions to the next time instant of .?+?1. The environment responds to the action (.?=?.) by putting the agent in a new state of .?=?. and providing feedback to 作者: 水獺 時(shí)間: 2025-3-22 18:17 作者: Ingredient 時(shí)間: 2025-3-22 21:52
https://doi.org/10.1007/978-3-642-70880-0rlo approach (MC), and finally using the temporal difference (TD) approach. In all these approaches, we always looked at problems where the state space and actions were both discrete. Only in the previous chapter toward the end did we talk about Q-learning in a continuous state space. We discretized作者: 外貌 時(shí)間: 2025-3-23 05:01 作者: ironic 時(shí)間: 2025-3-23 06:18
What Is the Microsoft HoloLens? a given current policy. In a second step, these estimated values were used to find a better policy by choosing the best action in a given state. These two steps were carried out in a loop again and again until no further improvement in values was observed. In this chapter, we will look at a differe作者: FLAT 時(shí)間: 2025-3-23 09:49 作者: 藥物 時(shí)間: 2025-3-23 13:55 作者: 首創(chuàng)精神 時(shí)間: 2025-3-23 20:14 作者: Torrid 時(shí)間: 2025-3-23 23:45 作者: gerontocracy 時(shí)間: 2025-3-24 05:04 作者: RALES 時(shí)間: 2025-3-24 10:28
Policy Gradient Algorithms,e two steps were carried out in a loop again and again until no further improvement in values was observed. In this chapter, we will look at a different approach for learning optimal policies by directly operating in the policy space. We will improve the policies without explicating learning or using state or state-action values.作者: 蘑菇 時(shí)間: 2025-3-24 13:33 作者: Vital-Signs 時(shí)間: 2025-3-24 17:34 作者: 酷熱 時(shí)間: 2025-3-24 21:06
Book 20211st editioninance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise..You‘ll begin by reviewing the Markov decision processes, Bellman equations, and dynamic programming that form the core concepts and foundation of deep reinforcem作者: dura-mater 時(shí)間: 2025-3-24 23:59
Marc Joseph Saugey Restoration,tic world, we would have a single pair of (., .) for a fixed combination of (., .). However, in stochastic environments, i.e., environments with uncertain outcomes, we could have many pairs of (., .) for a given (., .).作者: 人類 時(shí)間: 2025-3-25 03:39 作者: 異端邪說下 時(shí)間: 2025-3-25 09:36 作者: ciliary-body 時(shí)間: 2025-3-25 12:21 作者: A精確的 時(shí)間: 2025-3-25 17:36 作者: DRAFT 時(shí)間: 2025-3-25 21:44
Function Approximation,oximating values, first with a linear approach that has a good theoretical foundation and then with a nonlinear approach specifically with neural networks. This aspect of combining deep learning with reinforcement learning is the most exciting development that has moved reinforcement learning algorithms to scale.作者: 舊石器時(shí)代 時(shí)間: 2025-3-26 00:29 作者: miscreant 時(shí)間: 2025-3-26 08:13 作者: chronicle 時(shí)間: 2025-3-26 10:27
CNN and deep q-networks.Explains deep-q learning and policyDeep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning 作者: 圓錐體 時(shí)間: 2025-3-26 13:26
Implementing Continuous Integrationsics and finish up with mastering some of the most recent developments in the field. There will be a good mix of theory (with minimal mathematics) and code implementations using PyTorch as well as TensorFlow.作者: 死貓他燒焦 時(shí)間: 2025-3-26 18:37
Integrating Testers into DevOpsrning are modeled as . (MDP), we start by first introducing Markov chains (MC) followed by Markov reward processes (MRP). We finish up by discussing MDP in-depth while covering model setup and the assumptions behind MDP.作者: Resign 時(shí)間: 2025-3-26 21:50
What Is the Microsoft HoloLens?e two steps were carried out in a loop again and again until no further improvement in values was observed. In this chapter, we will look at a different approach for learning optimal policies by directly operating in the policy space. We will improve the policies without explicating learning or using state or state-action values.作者: cuticle 時(shí)間: 2025-3-27 01:39 作者: Indurate 時(shí)間: 2025-3-27 06:44 作者: 駭人 時(shí)間: 2025-3-27 11:49
Introduction to Reinforcement Learning,as led to many significant advances that are increasingly getting machines closer to acting the way humans do. In this book, we will start with the basics and finish up with mastering some of the most recent developments in the field. There will be a good mix of theory (with minimal mathematics) and作者: AER 時(shí)間: 2025-3-27 13:40
Markov Decision Processes,ic processes under the branch of probability that models sequential decision-making behavior. While most of the problems we study in reinforcement learning are modeled as . (MDP), we start by first introducing Markov chains (MC) followed by Markov reward processes (MRP). We finish up by discussing M作者: 使害羞 時(shí)間: 2025-3-27 20:04 作者: 評(píng)論性 時(shí)間: 2025-3-28 01:05 作者: stroke 時(shí)間: 2025-3-28 03:05 作者: 保守 時(shí)間: 2025-3-28 09:39
Deep Q-Learning,learning using neural networks is also known as . (DQN). We will first summarize what we have talked about so far with respect to Q-learning. We will then look at code implementations of DQN on simple problems followed by training an agent to play Atari games. Following this, we will extend our know作者: 鄙視讀作 時(shí)間: 2025-3-28 14:01
Policy Gradient Algorithms, a given current policy. In a second step, these estimated values were used to find a better policy by choosing the best action in a given state. These two steps were carried out in a loop again and again until no further improvement in values was observed. In this chapter, we will look at a differe作者: 慢慢沖刷 時(shí)間: 2025-3-28 15:04 作者: 蝕刻術(shù) 時(shí)間: 2025-3-28 20:05 作者: 種類 時(shí)間: 2025-3-28 23:12 作者: Detonate 時(shí)間: 2025-3-29 04:59 作者: 箴言 時(shí)間: 2025-3-29 08:50
Model-Free Approaches,lculate the exact transition probabilities from one state to another state but easy to sample states from an environment. To summarize, we use model-free methods when either we do not know the model dynamics or we know the model, but it is much more practical to sample than to calculate the transiti作者: Bridle 時(shí)間: 2025-3-29 11:24 作者: ABASH 時(shí)間: 2025-3-29 17:00
Book 20211st edition), which played a key role inthe success of AlphaGo. The final chapters conclude with deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you‘ll understand deep reinforcement learning along with deep q networks and policy grad作者: 不易燃 時(shí)間: 2025-3-29 23:15
Einleitung,, ein ver?ndertes Netzbauverhalten zeigen. Das ver?nderte Verhalten ist im fertigen Netz abzulesen und kann dort gemessen werden. Die Ver?nderungen sind gro?enteils für die gegebene Substanz charakteristisch.
