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Found 155 papers, 21 papers with code
On Semiparametric Exponential Family Graphical Models
We propose a new class of semiparametric exponential family graphical models for the analysis of high dimensional mixed data.
Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
To recover $\beta^*$, we propose an $\ell_1$-regularized least-squares estimator.
Human Memory Search as Initial-Visit Emitting Random Walk
In this paper, we propose the first efficient maximum likelihood estimate (MLE) for INVITE by decomposing the censored output into a series of absorbing random walks.
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation
We consider estimating the parametric component of single index models in high dimensions.
On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models
We consider estimating the parametric components of semi-parametric multiple index models in a high-dimensional and non-Gaussian setting.
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma
We consider estimating the parametric components of semiparametric multi-index models in high dimensions.
Misspecified Nonconvex Statistical Optimization for Phase Retrieval
Existing nonconvex statistical optimization theory and methods crucially rely on the correct specification of the underlying "true" statistical models.
PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE
Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either discrete or continuous space.
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
To this end, we propose two decentralized actor-critic algorithms with function approximation, which are applicable to large-scale MARL problems where both the number of states and the number of agents are massively large.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Despite the success of single-agent reinforcement learning, multi-agent reinforcement learning (MARL) remains challenging due to complex interactions between agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference
We study the hypothesis testing problem of inferring the existence of combinatorial structures in undirected graphical models.
Tensor Methods for Additive Index Models under Discordance and Heterogeneity
Motivated by the sampling problems and heterogeneity issues common in high- dimensional big datasets, we consider a class of discordant additive index models.
Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval
We study the fundamental tradeoffs between statistical accuracy and computational tractability in the analysis of high dimensional heterogeneous data.
Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective
We study reinforcement learning algorithms with nonlinear function approximation in the online setting.
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely.
High-dimensional Varying Index Coefficient Models via Stein's Identity
We study the parameter estimation problem for a varying index coefficient model in high dimensions.
Provable Gaussian Embedding with One Observation
In this paper, we study the Gaussian embedding model and develop the first theoretical results for exponential family embedding models.
Contrastive Learning from Pairwise Measurements
In this paper, we study a semiparametric model where the pairwise measurements follow a natural exponential family distribution with an unknown base measure.
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents
This work appears to be the first finite-sample analysis for batch MARL, a step towards rigorous theoretical understanding of general MARL algorithms in the finite-sample regime.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Theoretical Analysis of Deep Q-Learning
Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood.
A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning
This paper extends off-policy reinforcement learning to the multi-agent case in which a set of networked agents communicating with their neighbors according to a time-varying graph collaboratively evaluates and improves a target policy while following a distinct behavior policy.
Neural Temporal-Difference and Q-Learning Provably Converge to Global Optima
Temporal-difference learning (TD), coupled with neural networks, is among the most fundamental building blocks of deep reinforcement learning.
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games
To the best of our knowledge, this work appears to be the first one to investigate the optimization landscape of LQ games, and provably show the convergence of policy optimization methods to the Nash equilibria.
Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy
Proximal policy optimization and trust region policy optimization (PPO and TRPO) with actor and critic parametrized by neural networks achieve significant empirical success in deep reinforcement learning.
A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning
This paper considers a distributed reinforcement learning problem in which a network of multiple agents aim to cooperatively maximize the globally averaged return through communication with only local neighbors.
Provably Efficient Reinforcement Learning with Linear Function Approximation
Modern Reinforcement Learning (RL) is commonly applied to practical problems with an enormous number of states, where function approximation must be deployed to approximate either the value function or the policy.
A Convergence Result for Regularized Actor-Critic Methods
In this paper, we present a probability one convergence proof, under suitable conditions, of a certain class of actor-critic algorithms for finding approximate solutions to entropy-regularized MDPs using the machinery of stochastic approximation.
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
We consider the weakly supervised binary classification problem where the labels are randomly flipped with probability $1- {\alpha}$.
On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost
Despite the empirical success of the actor-critic algorithm, its theoretical understanding lags behind.
Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization
We study the policy evaluation problem in multi-agent reinforcement learning where a group of agents, with jointly observed states and private local actions and rewards, collaborate to learn the value function of a given policy via local computation and communication over a connected undirected network.
Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis
Specifically, we consider a new framework for this problem where the sparsity is implicitly enforced via mapping a low dimensional representation $x_0 \in \mathbb{R}^k$ through a known $n$-layer ReLU generative network $G:\mathbb{R}^k\rightarrow\mathbb{R}^d$ such that $\theta_0 = G(x_0)$.
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
In detail, we prove that neural natural policy gradient converges to a globally optimal policy at a sublinear rate.
Robust One-Bit Recovery via ReLU Generative Networks: Improved Statistical Rate and Global Landscape Analysis
In this paper, we consider a new framework for the one-bit sensing problem where the sparsity is implicitly enforced via mapping a low dimensional representation $x_0$ through a known $n$-layer ReLU generative network $G:\mathbb{R}^k\rightarrow\mathbb{R}^d$.
Credible Sample Elicitation by Deep Learning, for Deep Learning
While classical elicitation results apply to eliciting a complex and generative (and continuous) distribution $p(x)$ for this image data, we are interested in eliciting samples $x_i \sim p(x)$ from agents.
Sample Elicitation
We also show a connection between this sample elicitation problem and $f$-GAN, and how this connection can help reconstruct an estimator of the distribution based on collected samples.
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games
We study discrete-time mean-field Markov games with infinite numbers of agents where each agent aims to minimize its ergodic cost.
Convergent Policy Optimization for Safe Reinforcement Learning
We study the safe reinforcement learning problem with nonlinear function approximation, where policy optimization is formulated as a constrained optimization problem with both the objective and the constraint being nonconvex functions.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Orthogonal to the existing reviews on MARL, we highlight several new angles and taxonomies of MARL theory, including learning in extensive-form games, decentralized MARL with networked agents, MARL in the mean-field regime, (non-)convergence of policy-based methods for learning in games, etc.
Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy
Proximal policy optimization and trust region policy optimization (PPO and TRPO) with actor and critic parametrized by neural networks achieve significant empirical success in deep reinforcement learning.
Variance Reduced Policy Evaluation with Smooth Function Approximation
Policy evaluation with smooth and nonlinear function approximation has shown great potential for reinforcement learning.
Neural Temporal-Difference Learning Converges to Global Optima
Temporal-difference learning (TD), coupled with neural networks, is among the most fundamental building blocks of deep reinforcement learning.
Statistical-Computational Tradeoff in Single Index Models
Using the statistical query model to characterize the computational cost of an algorithm, we show that when $\cov(Y, X^\top\beta^*)=0$ and $\cov(Y,(X^\top\beta^*)^2)>0$, no computationally tractable algorithms can achieve the information-theoretic limit of the minimax risk.
Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost
Despite the empirical success of the actor-critic algorithm, its theoretical understanding lags behind.
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances
Multi-agent reinforcement learning (MARL) has long been a significant and everlasting research topic in both machine learning and control.
Provably Efficient Exploration in Policy Optimization
While policy-based reinforcement learning (RL) achieves tremendous successes in practice, it is significantly less understood in theory, especially compared with value-based RL.
Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator
Multi-agent reinforcement learning has been successfully applied to a number of challenging problems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework
This paper develops a Pontryagin Differentiable Programming (PDP) methodology, which establishes a unified framework to solve a broad class of learning and control tasks.
On Computation and Generalization of Generative Adversarial Imitation Learning
Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for learning sequential decision-making policies.
Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium
In the offline setting, we control both players and aim to find the Nash Equilibrium by minimizing the duality gap.
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization
To this end, we present an \underline{O}ptimistic \underline{P}rimal-\underline{D}ual Proximal Policy \underline{OP}timization (OPDOP) algorithm where the value function is estimated by combining the least-squares policy evaluation and an additional bonus term for safe exploration.
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss
In particular, we prove that the proposed algorithm achieves $\widetilde{\mathcal{O}}(L|\mathcal{S}|\sqrt{|\mathcal{A}|T})$ upper bounds of both the regret and the constraint violation, where $L$ is the length of each episode.
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
We consider the bipartite graph and formalize its representation learning problem as a statistical estimation problem of parameters in a semiparametric exponential family distribution.
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate
Generative adversarial imitation learning (GAIL) demonstrates tremendous success in practice, especially when combined with neural networks.
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
We aim to answer the following questions: When the function approximator is a neural network, how does the associated feature representation evolve?
Neural Certificates for Safe Control Policies
This paper develops an approach to learn a policy of a dynamical system that is guaranteed to be both provably safe and goal-reaching.
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
We highlight that MF-FQI algorithm enjoys a "blessing of many agents" property in the sense that a larger number of observed agents improves the performance of MF-FQI algorithm.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data
Empowered by expressive function approximators such as neural networks, deep reinforcement learning (DRL) achieves tremendous empirical successes.
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
We study risk-sensitive reinforcement learning in episodic Markov decision processes with unknown transition kernels, where the goal is to optimize the total reward under the risk measure of exponential utility.
On the Global Optimality of Model-Agnostic Meta-Learning
Model-agnostic meta-learning (MAML) formulates meta-learning as a bilevel optimization problem, where the inner level solves each subtask based on a shared prior, while the outer level searches for the optimal shared prior by optimizing its aggregated performance over all the subtasks.
Dynamic Regret of Policy Optimization in Non-stationary Environments
We consider reinforcement learning (RL) in episodic MDPs with adversarial full-information reward feedback and unknown fixed transition kernels.
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation.
A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic
Bilevel optimization is a class of problems which exhibit a two-level structure, and its goal is to minimize an outer objective function with variables which are constrained to be the optimal solution to an (inner) optimization problem.
Understanding Implicit Regularization in Over-Parameterized Single Index Model
For both the vector and matrix settings, we construct an over-parameterized least-squares loss function by employing the score function transform and a robust truncation step designed specifically for heavy-tailed data.
Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy
To the best of our knowledge, we establish the rate of convergence and global optimality of single-timescale actor-critic with linear function approximation for the first time.
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time
Reinforcement learning is a powerful tool to learn the optimal policy of possibly multiple agents by interacting with the environment.
Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning
Existing approaches for this problem are based on two-timescale or double-loop stochastic gradient algorithms, which may also require sampling large-batch data.
Provable Fictitious Play for General Mean-Field Games
We propose a reinforcement learning algorithm for stationary mean-field games, where the goal is to learn a pair of mean-field state and stationary policy that constitutes the Nash equilibrium.
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces
The classical theory of reinforcement learning (RL) has focused on tabular and linear representations of value functions.
Provably Efficient Neural GTD for Off-Policy Learning
This paper studies a gradient temporal difference (GTD) algorithm using neural network (NN) function approximators to minimize the mean squared Bellman error (MSBE).
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach
We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation.
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations
Reinforcement learning (RL) algorithms combined with modern function approximators such as kernel functions and deep neural networks have achieved significant empirical successes in large-scale application problems with a massive number of states.
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Temporal-difference and Q-learning play a key role in deep reinforcement learning, where they are empowered by expressive nonlinear function approximators such as neural networks.
Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization
Specifically, we prove that moving along the geodesic in the direction of functional gradient with respect to the second-order Wasserstein distance is equivalent to applying a pushforward mapping to a probability distribution, which can be approximated accurately by pushing a set of particles.
Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy
In particular, we focus on a variance-constrained policy optimization problem where the goal is to find a policy that maximizes the expected value of the long-run average reward, subject to a constraint that the long-run variance of the average reward is upper bounded by a threshold.
Is Pessimism Provably Efficient for Offline RL?
We study offline reinforcement learning (RL), which aims to learn an optimal policy based on a dataset collected a priori.
Offline Policy Optimization with Variance Regularization
Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications.
Policy Optimization in Zero-Sum Markov Games: Fictitious Self-Play Provably Attains Nash Equilibria
Specifically, in each iteration, each player infers the policy of the opponent implicitly via policy evaluation and improves its current policy by taking the smoothed best-response via a proximal policy optimization (PPO) step.
Optimistic Policy Optimization with General Function Approximations
Although policy optimization with neural networks has a track record of achieving state-of-the-art results in reinforcement learning on various domains, the theoretical understanding of the computational and sample efficiency of policy optimization remains restricted to linear function approximations with finite-dimensional feature representations, which hinders the design of principled, effective, and efficient algorithms.
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation
We study risk-sensitive reinforcement learning with the entropic risk measure and function approximation.
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum
We focus on bilevel problems where the lower level subproblem is strongly-convex and the upper level objective function is smooth.
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
Instrumental variables (IVs), in the context of RL, are the variables whose influence on the state variables are all mediated through the action.
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality
We also show that the overall convergence of DR-Off-PAC is doubly robust to the approximation errors that depend only on the expressive power of approximation functions.
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle.
Gap-Dependent Bounds for Two-Player Markov Games
As one of the most popular methods in the field of reinforcement learning, Q-learning has received increasing attention.
A Unified Off-Policy Evaluation Approach for General Value Function
We further show that unlike GTD, the learned GVFs by GenTD are guaranteed to converge to the ground truth GVFs as long as the function approximation power is sufficiently large.
Towards General Function Approximation in Zero-Sum Markov Games
In the {coordinated} setting where both players are controlled by the agent, we propose a model-based algorithm and a model-free algorithm.
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning
The recent emergence of reinforcement learning has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms.
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation
In generative adversarial imitation learning (GAIL), the agent aims to learn a policy from an expert demonstration so that its performance cannot be discriminated from the expert policy on a certain predefined reward set.
Can Reinforcement Learning Efficiently Find Stackelberg-Nash Equilibria in General-Sum Markov Games?
To our best knowledge, we establish the first provably efficient RL algorithms for solving SNE in general-sum Markov games with leader-controlled state transitions.
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory
This paper proposes a new algorithm for learning the optimal policies under a novel multi-agent predictive state representation reinforcement learning model.
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence
To regulate a social system comprised of self-interested agents, economic incentives are often required to induce a desirable outcome.
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs
We study episodic reinforcement learning (RL) in non-stationary linear kernel Markov decision processes (MDPs).
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game
Then, given any extrinsic reward, the agent computes the policy via a planning algorithm with offline data collected in the exploration phase.
False Correlation Reduction for Offline Reinforcement Learning
Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems.
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning
The exponential Bellman equation inspires us to develop a novel analysis of Bellman backup procedures in risk-sensitive RL algorithms, and further motivates the design of a novel exploration mechanism.
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL
Theoretically, under a weak coverage assumption that the experience dataset contains enough information about the optimal policy, we prove that for an episodic mean-field MDP with a horizon $H$ and $N$ training trajectories, SAFARI attains a sub-optimality gap of $\mathcal{O}(H^2d_{\rm eff} /\sqrt{N})$, where $d_{\rm eff}$ is the effective dimension of the function class for parameterizing the value function, but independent on the number of agents.
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration
In constrained multi-objective RL, the goal is to learn a policy that achieves the best performance specified by a multi-objective preference function under a constraint.
Multi-Objective Reinforcement Learning
reinforcement-learning
BooVI: Provably Efficient Bootstrapped Value Iteration
Despite the tremendous success of reinforcement learning (RL) with function approximation, efficient exploration remains a significant challenge, both practically and theoretically.
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
In this paper, we present a scalable and elastic library ElegantRL-podracer for cloud-native deep reinforcement learning, which efficiently supports millions of GPU cores to carry out massively parallel training at multiple levels.
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
Specifically, we consider a version of AC where the actor and critic are represented by overparameterized two-layer neural networks and are updated with two-timescale learning rates.
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers?
We develop sample-efficient reinforcement learning (RL) algorithms for solving for an SNE in both online and offline settings.
Exponential Family Model-Based Reinforcement Learning via Score Matching
We propose an optimistic model-based algorithm, dubbed SMRL, for finite-horizon episodic reinforcement learning (RL) when the transition model is specified by exponential family distributions with $d$ parameters and the reward is bounded and known.
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning
In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties (e. g., safety, stability) under the learned controller.
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
When the dataset does not have uniform coverage over all policy pairs, finding an approximate NE involves challenges in three aspects: (i) distributional shift between the behavior policy and the optimal policy, (ii) function approximation to handle large state space, and (iii) minimax optimization for equilibrium solving.
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
This paper proposes a novel model of sequential information design, namely the Markov persuasion processes (MPPs), where a sender, with informational advantage, seeks to persuade a stream of myopic receivers to take actions that maximizes the sender's cumulative utilities in a finite horizon Markovian environment with varying prior and utility functions.
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
We show that such OOD sampling and pessimistic bootstrapping yields provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL.
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach
Dynamic mechanism design studies how mechanism designers should allocate resources among agents in a time-varying environment.
The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches
In this paper, we study the problem of regret minimization for episodic Reinforcement Learning (RL) both in the model-free and the model-based setting.
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market.
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
The sample efficiency of OP-TENET is enabled by a sequence of ingredients: (i) a Bellman operator with finite memory, which represents the value function in a recursive manner, (ii) the identification and estimation of such an operator via an adversarial integral equation, which features a smoothed discriminator tailored to the linear structure, and (iii) the exploration of the observation and state spaces via optimism, which is based on quantifying the uncertainty in the adversarial integral equation.
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning
In the setting where the function approximation is employed to handle large state spaces, with only mild assumptions on the expressiveness of the function class, we are able to design a dynamic mechanism using offline reinforcement learning algorithms.
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation
To the best of our knowledge, this is the first theoretical result for PbRL with (general) function approximation.
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency
For a class of POMDPs with a low-rank structure in the transition kernel, ETC attains an $O(1/\epsilon^2)$ sample complexity that scales polynomially with the horizon and the intrinsic dimension (that is, the rank).
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
We study offline reinforcement learning (RL) in partially observable Markov decision processes.
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games
We study decentralized policy learning in Markov games where we control a single agent to play with nonstationary and possibly adversarial opponents.
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
Our algorithms feature a combination of Upper Confidence Bound (UCB)-type optimism and fictitious play under the scope of simultaneous policy optimization in a non-stationary environment.
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
Moreover, under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs.
Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments
We study offline reinforcement learning under a novel model called strategic MDP, which characterizes the strategic interactions between a principal and a sequence of myopic agents with private types.
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes
Due to the lack of online interaction with the environment, offline RL is facing the following two significant challenges: (i) the agent may be confounded by the unobserved state variables; (ii) the offline data collected a prior does not provide sufficient coverage for the environment.
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL
The cooperative Multi-A gent R einforcement Learning (MARL) with permutation invariant agents framework has achieved tremendous empirical successes in real-world applications.
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an unknown and stochastic environment under hard constraints that require the system state not to reach certain specified unsafe regions.
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design
First, from the seller's perspective, we need to efficiently explore the environment in the presence of potentially nontruthful bidders who aim to manipulates seller's policy.
GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond
The proposed algorithm modifies the standard posterior sampling algorithm in two aspects: (i) we use an optimistic prior distribution that biases towards hypotheses with higher values and (ii) a loglikelihood function is set to be the empirical loss evaluated on the historical data, where the choice of loss function supports both model-free and model-based learning.
The Sample Complexity of Online Contract Design
This result shows that exponential-in-$m$ samples are sufficient and necessary to learn a near-optimal contract, resolving an open problem on the hardness of online contract design.
Policy learning "without'' overlap: Pessimism and generalized empirical Bernstein's inequality
Existing policy learning methods rely on a uniform overlap assumption, i. e., the propensities of exploring all actions for all individual characteristics are lower bounded in the offline dataset.
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information
To tackle the distributional mismatch, we leverage the idea of pessimism and use our OPE method to develop an off-policy learning algorithm for finding a desirable policy pair for both Alice and Bob.
Offline Policy Optimization in RL with Variance Regularizaton
Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications.
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning
We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market.
Can We Find Nash Equilibria at a Linear Rate in Markov Games?
Second, when both players adopt the algorithm, their joint policy converges to a Nash equilibrium of the game.
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model
Such a problem is modeled as a Stackelberg game between the principal and the agent, where the principal announces a scoring rule that specifies the payment, and then the agent then chooses an effort level that maximizes her own profit and reports the information.
A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observations
We study the offline contextual bandit problem, where we aim to acquire an optimal policy using observational data.
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
This gives a deeper understanding of why the in-sample learning paradigm works, i. e., it applies implicit value regularization to the policy.
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning
Motivated by the observation, we present a multi-agent PPO algorithm in which the local policy of each agent is updated similarly to vanilla PPO.
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
To achieve this, existing sample-efficient online RL algorithms typically consist of three components: estimation, planning, and exploration.
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
Specifically, we propose Multi-Task Diffusion Model (\textsc{MTDiff}), a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis in multi-task offline settings.
Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism
In this paper, we study offline Reinforcement Learning with Human Feedback (RLHF) where we aim to learn the human's underlying reward and the MDP's optimal policy from a set of trajectories induced by human choices.
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization
(b) What is a proper performance metric for ICL and what is the error rate?
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning
We examine online safe multi-agent reinforcement learning using constrained Markov games in which agents compete by maximizing their expected total rewards under a constraint on expected total utilities.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dimensional raw observations to a compact representation and uses it for more efficient exploration and planning.
A General Framework for Sequential Decision-Making under Adaptivity Constraints
We take the first step in studying general sequential decision-making under two adaptivity constraints: rare policy switch and batch learning.
Contextual Dynamic Pricing with Strategic Buyers
We first prove that existing non-strategic pricing policies that neglect the buyers' strategic behavior result in a linear $\Omega(T)$ regret with $T$ the total time horizon, indicating that these policies are not better than a random pricing policy.
Actions Speak What You Want: Provably Sample-Efficient Reinforcement Learning of the Quantal Stackelberg Equilibrium from Strategic Feedbacks
The goal of the leader is to find her optimal policy, which yields the optimal expected total return, by interacting with the follower and learning from data.
Sample-Efficient Multi-Agent RL: An Optimization Perspective
We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation.
Learning Regularized Graphon Mean-Field Games with Unknown Graphons
Second, using kernel embedding of distributions, we design efficient algorithms to estimate the transition kernels, reward functions, and graphons from sampled agents.
Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks
For these settings, we design a provably sample-efficient algorithm which achieves a $ \mathcal{\tilde O}(s_0^2 \log^2 T)$ regret in the sparse case and $ \mathcal{\tilde O} ( r ^2 \log^2 T)$ regret in the low-rank case, using only $L = \mathcal{O}( \log T)$ batches.
Empowering Autonomous Driving with Large Language Models: A Safety Perspective
Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen driving scenarios, largely stemming from the non-interpretability and poor generalization of the deep neural networks within the AD system, particularly in out-of-distribution and uncertain data.
Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems
In this paper, we extend mean-field Langevin dynamics to minimax optimization over probability distributions for the first time with symmetric and provably convergent updates.
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF
But bilevel problems such as incentive design, inverse reinforcement learning (RL), and RL from human feedback (RLHF) are often modeled as dynamic objective functions that go beyond the simple static objective structures, which pose significant challenges of using existing bilevel solutions.
Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning
Designing algorithms for a constrained convex MDP faces several challenges, including (1) handling the large state space, (2) managing the exploration/exploitation tradeoff, and (3) solving the constrained optimization where the objective and the constraint are both nonlinear functions of the visitation measure.
Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality
In addition, we prove that an interesting "task allocation" phenomenon emerges during the gradient flow dynamics, where each attention head focuses on solving a single task of the multi-task model.
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games
In this paper, we argue for the perspective that explicit representation of information structures is an important component of analyzing and solving reinforcement learning problems.
Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory
Conditional diffusion models serve as the foundation of modern image synthesis and find extensive application in fields like computational biology and reinforcement learning.
A Mean-Field Analysis of Neural Gradient Descent-Ascent: Applications to Functional Conditional Moment Equations
Under this regime, gradient descent-ascent corresponds to a Wasserstein gradient flow over the space of probability measures defined over the space of neural network parameters.
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate
Generative adversarial imitation learning (GAIL) demonstrates tremendous success in practice, especially when combined with neural networks.
Breaking the Curse of Many Agents: Provable Mean Embedding $Q$-Iteration for Mean-Field Reinforcement Learning
We highlight that MF-FQI algorithm enjoys a ``blessing of many agents'' property in the sense that a larger number of observed agents improves the performance of MF-FQI algorithm.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
