Titlebook: Deep Reinforcement Learning; Fundamentals, Resear Hao Dong,Zihan Ding,Shanghang Zhang Book 2020 Springer Nature Singapore Pte Ltd. 2020 Dee

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書目名稱Deep Reinforcement Learning影響因子(影響力)




書目名稱Deep Reinforcement Learning影響因子(影響力)學(xué)科排名




書目名稱Deep Reinforcement Learning網(wǎng)絡(luò)公開度




書目名稱Deep Reinforcement Learning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Reinforcement Learning被引頻次




書目名稱Deep Reinforcement Learning被引頻次學(xué)科排名




書目名稱Deep Reinforcement Learning年度引用




書目名稱Deep Reinforcement Learning年度引用學(xué)科排名




書目名稱Deep Reinforcement Learning讀者反饋




書目名稱Deep Reinforcement Learning讀者反饋學(xué)科排名

palliative-care

OPINE

Parallel

brother

Cloudburst

Combine Deep ,-Networks with Actor-Criticral networks to approximate the optimal action-value functions. It receives only the pixels as inputs and achieves human-level performance on Atari games. Actor-critic methods transform the Monte Carlo update of the REINFORCE algorithm into the temporal-difference update for learning the policy para

Cloudburst

Challenges of Reinforcement Learning; (2) stability of training; (3) the catastrophic interference problem; (4) the exploration problems; (5) meta-learning and representation learning for the generality of reinforcement learning methods across tasks; (6) multi-agent reinforcement learning with other agents as part of the environment;

移動(dòng)

Imitation Learningtential approaches, which leverages the expert demonstrations in sequential decision-making process. In order to provide the readers a comprehensive understanding about how to effectively extract information from the demonstration data, we introduce the most important categories in imitation learnin

Interstellar

Endearing