Search Results for author:
Found 9 papers, 0 papers with code
Meta-Task Planning for Language Agents
Effective planning is crucial for the success of LLM agents in real-world tasks, making it a highly pursued topic in the community.
Aligning Crowd Feedback via Distributional Preference Reward Modeling
We propose the Distributional Preference Reward Model (DPRM), a simple yet effective framework to align large language models with diverse human preferences.
Enhancing the Hierarchical Environment Design via Generative Trajectory Modeling
Unsupervised Environment Design (UED) is a paradigm for automatically generating a curriculum of training environments, enabling agents trained in these environments to develop general capabilities, i. e., achieving good zero-shot transfer performance.
Diversity Induced Environment Design via Self-Play
In this paper, we aim to introduce diversity in the Unsupervised Environment Design (UED) framework.
Generalization through Diversity: Improving Unsupervised Environment Design
Agent decision making using Reinforcement Learning (RL) heavily relies on either a model or simulator of the environment (e. g., moving in an 8x8 maze with three rooms, playing Chess on an 8x8 board).
Hidden State Approximation in Recurrent Neural Networks Using Continuous Particle Filtering
Using historical data to predict future events has many applications in the real world, such as stock price prediction; the robot localization.
Towards Soft Fairness in Restless Multi-Armed Bandits
To avoid starvation in the executed interventions across individuals/regions/communities, we first provide a soft fairness constraint and then provide an approach to enforce the soft fairness constraint in RMABs.
Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits
In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value.
CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks
Influence maximization is the problem of finding a small subset of nodes in a network that can maximize the diffusion of information.
