Domain-Aware Multiagent Reinforcement Learning in Navigation
暂无分享,去创建一个
[1] Satinder Singh,et al. On Learning Intrinsic Rewards for Policy Gradient Methods , 2018, NeurIPS.
[2] Furong Huang,et al. Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning , 2020, AAMAS.
[3] Felipe Leno da Silva,et al. A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems , 2019, J. Artif. Intell. Res..
[4] Sergey Levine,et al. Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor , 2018, ICML.
[5] Fei Sha,et al. Actor-Attention-Critic for Multi-Agent Reinforcement Learning , 2018, ICML.
[6] Jia Shi,et al. Model Predictive Control Guided Reinforcement Learning Control Scheme , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).
[7] Pieter Abbeel,et al. Emergence of Grounded Compositional Language in Multi-Agent Populations , 2017, AAAI.
[8] Timothy Verstraeten,et al. Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping , 2020, ArXiv.
[9] Tamer Basar,et al. Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms , 2019, Handbook of Reinforcement Learning and Control.
[10] Michael L. Littman,et al. Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.
[11] Andrew Y. Ng,et al. Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping , 1999, ICML.
[12] Peter Sanders,et al. Engineering Route Planning Algorithms , 2009, Algorithmics of Large and Complex Networks.
[13] Pieter Abbeel,et al. Benchmarking Model-Based Reinforcement Learning , 2019, ArXiv.
[14] Victor Lesser,et al. ROMA: Multi-Agent Reinforcement Learning with Emergent Roles , 2020, ICML.
[15] De-Chuan Zhan,et al. Automatic Successive Reinforcement Learning with Multiple Auxiliary Rewards , 2019, IJCAI.
[16] Fabien Michel,et al. Input Addition and Deletion in Reinforcement: Towards Learning with Structural Changes , 2020, AAMAS.
[17] Yi Wu,et al. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments , 2017, NIPS.
[18] Daqiang Zhang,et al. Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination , 2016, Comput. Networks.
[19] Christian Bauckhage,et al. Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures , 2019, ICANN.
[20] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[21] Peter Stone,et al. Transfer Learning for Reinforcement Learning Domains: A Survey , 2009, J. Mach. Learn. Res..
[22] Drew Wicke,et al. Multiagent Soft Q-Learning , 2018, AAAI Spring Symposia.
[23] Wenlong Fu,et al. Model-based reinforcement learning: A survey , 2018 .
[24] Matthew E. Taylor,et al. A survey and critique of multiagent deep reinforcement learning , 2019, Autonomous Agents and Multi-Agent Systems.
[25] Shimon Whiteson,et al. Counterfactual Multi-Agent Policy Gradients , 2017, AAAI.
[26] Junhyuk Oh,et al. What Can Learned Intrinsic Rewards Capture? , 2019, ICML.
[28] Shou-De Lin,et al. Designing Non-greedy Reinforcement Learning Agents with Diminishing Reward Shaping , 2018, AIES.
[29] Keeheon Lee,et al. The Computational Limits of Deep Learning , 2020, ArXiv.