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Found 16 papers, 4 papers with code
Regularized Q-Learning with Linear Function Approximation
Several successful reinforcement learning algorithms make use of regularization to promote multi-modal policies that exhibit enhanced exploration and robustness.
Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference
Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles.
A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning
Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient, adaptive, and explainable by learning an explicit model of the environment.
A Bayesian Approach to Robust Inverse Reinforcement Learning
We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL).
An active inference model of car following: Advantages and applications
We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models.
When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent.
Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees
Other approaches in the inverse reinforcement learning (IRL) literature emphasize policy estimation at the expense of reduced reward estimation accuracy.
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
To reduce the computational burden of a nested loop, novel methods such as SQIL [1] and IQ-Learn [2] emphasize policy estimation at the expense of reward estimation accuracy.
Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees
The flexibility in our design allows the proposed MARL-CAC algorithm to be used in a {\it fully decentralized} setting, where the agents can only communicate with their neighbors, as well as a {\it federated} setting, where the agents occasionally communicate with a server while optimizing their (partially personalized) local models.
Decentralized Riemannian Gradient Descent on the Stiefel Manifold
The global function is represented as a finite sum of smooth local functions, where each local function is associated with one agent and agents communicate with each other over an undirected connected graph.
On the Local Linear Rate of Consensus on the Stiefel Manifold
We study the convergence properties of Riemannian gradient method for solving the consensus problem (for an undirected connected graph) over the Stiefel manifold.
Structural Estimation of Partially Observable Markov Decision Processes
In this context, replacement decisions must be made under partial/imperfect information on the true state (i. e. condition of the equipment).
On Distributed Non-convex Optimization: Projected Subgradient Method For Weakly Convex Problems in Networks
In this paper, we propose a distributed implementation of the stochastic subgradient method with a theoretical guarantee.
Distributed Networked Learning with Correlated Data
We consider a distributed estimation method in a setting with heterogeneous streams of correlated data distributed across nodes in a network.
Swarming for Faster Convergence in Stochastic Optimization
We study a distributed framework for stochastic optimization which is inspired by models of collective motion found in nature (e. g., swarming) with mild communication requirements.
Zeroth Order Nonconvex Multi-Agent Optimization over Networks
In this paper, we consider distributed optimization problems over a multi-agent network, where each agent can only partially evaluate the objective function, and it is allowed to exchange messages with its immediate neighbors.
