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Found 3 papers, 1 papers with code
Hexa: Self-Improving for Knowledge-Grounded Dialogue System
A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e. g., web-search, memory retrieval) with modular approaches.
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
In order to resolve these issues, in this paper, we propose a learnable hash-based episodic count, which we name LECO, that efficiently performs as a task-specific intrinsic reward in hard exploration problems.
Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning
However, to the best of our knowledge, current research has yet to produce a result that has surpassed human-level performance in modern complex fighting games.
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