Search Results for author:
Found 8 papers, 4 papers with code
Protecting Your LLMs with Information Bottleneck
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content.
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency.
Mildly Constrained Evaluation Policy for Offline Reinforcement Learning
Offline reinforcement learning (RL) methodologies enforce constraints on the policy to adhere closely to the behavior policy, thereby stabilizing value learning and mitigating the selection of out-of-distribution (OOD) actions during test time.
Relabeling Minimal Training Subset to Flip a Prediction
When facing an unsatisfactory prediction from a machine learning model, users can be interested in investigating the underlying reasons and exploring the potential for reversing the outcome.
Elastic Monte Carlo Tree Search with State Abstraction for Strategy Game Playing
In this paper, we propose Elastic MCTS, an algorithm that uses state abstraction to play strategy games.
Portfolio Search and Optimization for General Strategy Game-Playing
Portfolio methods represent a simple but efficient type of action abstraction which has shown to improve the performance of search-based agents in a range of strategy games.
Generating Diverse and Competitive Play-Styles for Strategy Games
Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research community has not found super-human performance yet, like strategy games.
Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network
The proposed feature augmentation strategy can be used in many deep multi-task learning models.
