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Found 6 papers, 2 papers with code
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning
To address this limitation, we propose Imagine, Initialize, and Explore (IIE), a novel method that offers a promising solution for efficient multi-agent exploration in complex scenarios.
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning
To ensure optimal consistency, the optimal node is required to be the unique STN.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset
As the first session-level Chinese dataset, CHASE contains two separate parts, i. e., 2, 003 sessions manually constructed from scratch (CHASE-C), and 3, 456 sessions translated from English SParC (CHASE-T).
Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning
Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning (MARL) methods with linear or monotonic value decomposition can not ensure the optimal consistency (i. e. the correspondence between the individual greedy actions and the maximal true Q value), leading to instability and poor coordination.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling
Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate with search systems.
Meta-evaluation of Conversational Search Evaluation Metrics
Conversational search systems, such as Google Assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues.
