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Found 33 papers, 18 papers with code
Social Environment Design
Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making.
AI For Global Climate Cooperation 2023 Competition Proceedings
On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively.
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning
We then show that our model-based meta-learning approach is cost-effective in intervening on bandit agents with unseen explore-exploit strategies.
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N
To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks.
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal
In a sequential setting with multiple Agents, RIRL shows opposing consequences of the Principal's inattention to different information channels: 1) inattention to Agents' outputs closes wage gaps based on ability differences; and 2) inattention to Agents' efforts induces a social dilemma dynamic in which Agents work harder, but essentially for free.
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning
We validate the learned solutions are $\epsilon$-meta-equilibria through best-response analyses, show that they align with economic intuitions, and show our approach can learn a spectrum of qualitatively distinct $\epsilon$-meta-equilibria in open RBC models.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Simulation Intelligence: Towards a New Generation of Scientific Methods
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
We present WarpDrive, a flexible, lightweight, and easy-to-use open-source RL framework that implements end-to-end deep multi-agent RL on a single GPU (Graphics Processing Unit), built on PyCUDA and PyTorch.
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist
Here we show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning (RL) and data-driven simulations.
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning
Here we show that machine-learning-based economic simulation is a powerful policy and mechanism design framework to overcome these limitations.
Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents
We study the problem of training a principal in a multi-agent general-sum game using reinforcement learning (RL).
Robustness Gym: Unifying the NLP Evaluation Landscape
Despite impressive performance on standard benchmarks, deep neural networks are often brittle when deployed in real-world systems.
Technology Readiness Levels for Machine Learning Systems
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
ERMAS: Learning Policies Robust to Reality Gaps in Multi-Agent Simulations
Policies for real-world multi-agent problems, such as optimal taxation, can be learned in multi-agent simulations with AI agents that emulate humans.
The Rise of AI-Driven Simulators: Building a New Crystal Ball
These advances may lead to a new era in computational simulation, in which sensors of many kinds are used to produce vast quantities of data, AI methods identify patterns in those data, and new AI-driven simulators combine machine-learned and mathematical rules to make accurate and actionable predictions.
ESPRIT: Explaining Solutions to Physical Reasoning Tasks
Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions.
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
In experiments conducted on MTurk, an AI tax policy provides an equality-productivity trade-off that is similar to that provided by the Saez framework along with higher inverse-income weighted social welfare.
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science.
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
For instance, in tasks where the agent must achieve some goal state, simple distance-to-goal reward shaping often fails, as it renders learning vulnerable to local optima.
Sketch-Fill-A-R: A Persona-Grounded Chit-Chat Generation Framework
Human-like chit-chat conversation requires agents to generate responses that are fluent, engaging and consistent.
Learning World Graph Decompositions To Accelerate Reinforcement Learning
Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents.
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
We perform a thorough ablation study to evaluate our approach on a suite of challenging maze tasks, demonstrating significant advantages from the proposed framework over baselines that lack world graph knowledge in terms of performance and efficiency.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
On the Generalization Gap in Reparameterizable Reinforcement Learning
For this problem class, estimating the expected return is efficient and the trajectory can be computed deterministically given peripheral random variables, which enables us to study reparametrizable RL using supervised learning and transfer learning theory.
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems.
Ranked #1 on
Multivariate Time Series Imputation
on PEMS-SF
Novel deep learning methods for track reconstruction
The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification.
High Energy Physics - Experiment Data Analysis, Statistics and Probability
Generating Multi-Agent Trajectories using Programmatic Weak Supervision
We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay.
Detecting Adversarial Examples via Neural Fingerprinting
Deep neural networks are vulnerable to adversarial examples, which dramatically alter model output using small input changes.
Multi-resolution Tensor Learning for Large-Scale Spatial Data
High-dimensional tensor models are notoriously computationally expensive to train.
Structured Exploration via Hierarchical Variational Policy Networks
Reinforcement learning in environments with large state-action spaces is challenging, as exploration can be highly inefficient.
Long-term Forecasting using Tensor-Train RNNs
We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics.
Long-term Forecasting using Higher Order Tensor RNNs
We present Higher-Order Tensor RNN (HOT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics.
Generating Long-term Trajectories Using Deep Hierarchical Networks
We study the problem of modeling spatiotemporal trajectories over long time horizons using expert demonstrations.
Improving the Robustness of Deep Neural Networks via Stability Training
In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network.
