Search Results for author:
Found 66 papers, 25 papers with code
M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality
Designing effective reward functions in multi-agent reinforcement learning (MARL) is a significant challenge, often leading to suboptimal or misaligned behaviors in complex, coordinated environments.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Grounded Persuasive Language Generation for Automated Marketing
This paper develops an agentic framework that employs large language models (LLMs) to automate the generation of persuasive and grounded marketing content, using real estate listing descriptions as our focal application domain.
Cooperative Strategic Planning Enhances Reasoning Capabilities in Large Language Models
However, the lack of effective cooperation between LLM agents hinders their performance, especially for multi-step reasoning tasks.
CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy
There is a significant gap between patient needs and available mental health support today.
TypedThinker: Typed Thinking Improves Large Language Model Reasoning
Despite significant advancements in the reasoning capabilities of Large Language Models (LLMs), the lack of diverse reasoning solutions often makes them trapped in a limited solution search area.
Leveraging a Cognitive Model to Measure Subjective Similarity of Human and GPT-4 Written Content
Cosine similarity between two documents can be computed using token embeddings formed by Large Language Models (LLMs) such as GPT-4, and used to categorize those documents across a range of uses.
Concept-Based Interpretable Reinforcement Learning with Limited to No Human Labels
To overcome this limitation, we introduce a novel training scheme that enables RL algorithms to efficiently learn a concept-based policy by only querying humans to label a small set of data, or in the extreme case, without any human labels.
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard
We study a multi-agent imitation learning (MAIL) problem where we take the perspective of a learner attempting to coordinate a group of agents based on demonstrations of an expert doing so.
Global Rewards in Restless Multi-Armed Bandits
Restless multi-armed bandits (RMAB) extend multi-armed bandits so pulling an arm impacts future states.
Safe Multi-agent Reinforcement Learning with Natural Language Constraints
These embeddings are then integrated into the multi-agent policy learning process, enabling agents to learn policies that minimize constraint violations while optimizing rewards.
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
We propose an interactive training scheme, PATIENT-{\Psi}-TRAINER, for mental health trainees to practice a key skill in CBT -- formulating the cognitive model of the patient -- through role-playing a therapy session with PATIENT-{\Psi}.
MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure
It learns to explore by first identifying the agents' high-rewarding joint state-action subspace from training tasks and then learning a set of diverse exploration policies to "cover" the subspace.
Where It Really Matters: Few-Shot Environmental Conservation Media Monitoring for Low-Resource Languages
Environmental conservation organizations routinely monitor news content on conservation in protected areas to maintain situational awareness of developments that can have an environmental impact.
On the Detection of Reviewer-Author Collusion Rings From Paper Bidding
A major threat to the peer-review systems of computer science conferences is the existence of "collusion rings" between reviewers.
Multi-defender Security Games with Schedules
We show that unlike prior work on multi-defender security games, the introduction of schedules can cause non-existence of equilibrium even under rather restricted environments.
Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?
Traffic simulators are used to generate data for learning in intelligent transportation systems (ITSs).
RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization
Landmines remain a threat to war-affected communities for years after conflicts have ended, partly due to the laborious nature of demining tasks.
Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game
To mitigate the intrinsic bias in language actions, our agents use an LLM to perform deductive reasoning and generate a diverse set of action candidates.
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning
In this work, we present a novel subgame curriculum learning framework for zero-sum games.
DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation
To address these limitations, we present a novel cascaded motion diffusion model, DiffDance, designed for high-resolution, long-form dance generation.
NewsPanda: Media Monitoring for Timely Conservation Action
We introduce NewsPanda, a toolkit which automatically detects and analyzes online articles related to environmental conservation and infrastructure construction.
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning
Unlike existing models, MABL is capable of encoding essential global information into the latent states during training while guaranteeing the decentralized execution of learned policies.
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game.
Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation
In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning.
Safe Subgame Resolving for Extensive Form Correlated Equilibrium
Correlated Equilibrium is a solution concept that is more general than Nash Equilibrium (NE) and can lead to outcomes with better social welfare.
Function Approximation for Solving Stackelberg Equilibrium in Large Perfect Information Games
Function approximation (FA) has been a critical component in solving large zero-sum games.
Neighborhood Adaptive Estimators for Causal Inference under Network Interference
In contrast to previous work, we approximate the relevant network interference patterns that lead to good estimates of the interference.
Video Background Music Generation: Dataset, Method and Evaluation
We believe our dataset, benchmark model, and evaluation metric will boost the development of video background music generation.
Explainable Action Advising for Multi-Agent Reinforcement Learning
This allows the student to self-reflect on what it has learned, enabling advice generalization and leading to improved sample efficiency and learning performance - even in environments where the teacher is sub-optimal.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks.
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation
we summarize three practical challenges which are not well solved for multi-scenario modeling: (1) Lacking of fine-grained and decoupled information transfer controls among multiple scenarios.
Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao
Therefore, it is crucial to perform cross-domain CTR prediction to transfer knowledge from large domains to small domains to alleviate the data sparsity issue.
Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignment
Many conferences rely on paper bidding as a key component of their reviewer assignment procedure.
A Dataset on Malicious Paper Bidding in Peer Review
The performance of these detection algorithms can be taken as a baseline for future research on detecting malicious bidding.
The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality
Traffic signal control (TSC) is a high-stakes domain that is growing in importance as traffic volume grows globally.
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning
The first algorithm, IVIPER, extends VIPER, a recent method for single-agent interpretable RL, to the multi-agent setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning
It seems likely that these patterns are shaped by the environment a speaker is exposed to in complex ways.
Ranked Prioritization of Groups in Combinatorial Bandit Allocation
Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land.
PerfectDou: Dominating DouDizhu with Perfect Information Distillation
To this end, we characterize card and game features for DouDizhu to represent the perfect and imperfect information.
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training
In this paper, we introduce a novel hierarchical formulation of robust RL - a general-sum Stackelberg game model called RRL-Stack - to formalize the sequential nature and provide extra flexibility for robust training.
A Survey of Explainable Reinforcement Learning
In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting.
Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer Ridesharing
Unlike commercial ridesharing, non-commercial peer-to-peer (P2P) ridesharing has been subject to limited research -- although it can promote viable solutions in non-urban communities.
Temporal Induced Self-Play for Stochastic Bayesian Games
One practical requirement in solving dynamic games is to ensure that the players play well from any decision point onward.
Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design
Many scientific conferences employ a two-phase paper review process, where some papers are assigned additional reviewers after the initial reviews are submitted.
Robust Reinforcement Learning Under Minimax Regret for Green Security
We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy.
Concadia: Towards Image-Based Text Generation with a Purpose
Current deep learning models often achieve excellent results on benchmark image-to-text datasets but fail to generate texts that are useful in practice.
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization
We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games.
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods
Because of this decision tree equivalence, any function approximator can be used during training, including a neural network, while yielding a decision tree policy for the base MDP.
Deep Archimedean Copulas
A central problem in machine learning and statistics is to model joint densities of random variables from data.
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security
Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i. e., patrollers), who must patrol vast areas to protect from attackers (e. g., poachers or illegal loggers).
Bandit Data-Driven Optimization
In this paper, we introduce bandit data-driven optimization, the first iterative prediction-prescription framework to address these pain points.
TPG-DNN: A Method for User Intent Prediction Based on Total Probability Formula and GRU Loss with Multi-task Learning
Critical as is to improve the online shopping experience for customers and merchants, how to find a proper approach for user intent prediction are paid great attention in both industry and academia.
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
We further consider the problem of restricting the joint probability that certain suspect pairs of reviewers are assigned to certain papers, and show that this problem is NP-hard for arbitrary constraints on these joint probabilities but efficiently solvable for a practical special case.
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the agent population is large.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Artificial Intelligence for Social Good: A Survey
Artificial intelligence for social good (AI4SG) is a research theme that aims to use and advance artificial intelligence to address societal issues and improve the well-being of the world.
AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline
With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems.
Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium
We show that a regret minimizer can be designed for a scaled extension of any two convex sets, and that from the decomposition we then obtain a global regret minimizer.
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning
Although existing works formulate this problem into a centralized learning with decentralized execution framework, which avoids the non-stationary problem in training, their decentralized execution paradigm limits the agents' capability to coordinate.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests
Transportation service providers that dispatch drivers and vehicles to riders start to support both on-demand ride requests posted in real time and rides scheduled in advance, leading to new challenges which, to the best of our knowledge, have not been addressed by existing works.
Learning and Planning in the Feature Deception Problem
In order to formally reason about deception, we introduce the feature deception problem (FDP), a domain-independent model and present a learning and planning framework for finding the optimal deception strategy, taking into account the adversary's preferences which are initially unknown to the defender.
Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games
With the recent advances in solving large, zero-sum extensive form games, there is a growing interest in the inverse problem of inferring underlying game parameters given only access to agent actions.
Draining the Water Hole: Mitigating Social Engineering Attacks with CyberTWEAK
Cyber adversaries have increasingly leveraged social engineering attacks to breach large organizations and threaten the well-being of today's online users.
Deep Reinforcement Learning for Green Security Games with Real-Time Information
Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing.
The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting
In this pilot study, we investigate (1) in what way a robot can express a certain mood to influence a human's decision making behavioral model; (2) how and to what extent the human will be influenced in a game theoretic setting.
What game are we playing? End-to-end learning in normal and extensive form games
Although recent work in AI has made great progress in solving large, zero-sum, extensive-form games, the underlying assumption in most past work is that the parameters of the game itself are known to the agents.
Designing the Game to Play: Optimizing Payoff Structure in Security Games
We study Stackelberg Security Games where the defender, in addition to allocating defensive resources to protect targets from the attacker, can strategically manipulate the attacker's payoff under budget constraints in weighted L^p-norm form regarding the amount of change.
