Reinforcement Learning
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
Runzhe Yang, Xingyuan Sun, Karthik Narasimhan
Neural Information Processing Systems http://nips.cc/
Synthesize Policies for Transfer and Adaptation across Tasks and Environments
Hexiang Hu, Liyu Chen, Boqing Gong, Fei Sha
Wefurther propose newtraining methods todisentangle the embeddings, making them both distinctive signatures of the environments and tasks and effective building blocks for composing the policies.
Learning Robust Options by Conditional Value at Risk Optimization
Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, Yoshimasa Tsuruoka
In the reinforcement learning context, anOption means a temporally extended sequence of actions [30],andisregarded asuseful formanypurposes, such asspeeding uplearning, transferring skills across domains, and solving long-term planning problems.
A Kernel Loss for Solving the Bellman Equation
Yihao Feng, Lihong Li, Qiang Liu
Neural Information Processing Systems http://nips.cc/
Provably Efficient Q-Learning with Low Switching Cost
Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
Neural Information Processing Systems http://nips.cc/
RethinkingIndividualGlobalMaxinCooperative Multi-AgentReinforcementLearning
Cooperative multi-agent reinforcement learning (MARL) has been proposed for multi-agent collaborations toaccomplish manychallenging tasks[1,2,3,4].
RethinkingIndividualGlobalMaxinCooperative Multi-AgentReinforcementLearning
Cooperative multi-agent reinforcement learning (MARL) has been proposed for multi-agent collaborations toaccomplish manychallenging tasks[1,2,3,4].
