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Found 24 papers, 4 papers with code
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates.
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation
We introduce EV3, a novel meta-optimization framework designed to efficiently train scalable machine learning models through an intuitive explore-assess-adapt protocol.
Factual and Personalized Recommendations using Language Models and Reinforcement Learning
Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences.
Demystifying Embedding Spaces using Large Language Models
Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format.
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
Modern recommender systems lie at the heart of complex ecosystems that couple the behavior of users, content providers, advertisers, and other actors.
Bayesian Regret Minimization in Offline Bandits
We study how to make decisions that minimize Bayesian regret in offline linear bandits.
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the observed outcomes.
Representation-Driven Reinforcement Learning
We present a representation-driven framework for reinforcement learning.
Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics
We highlight the importance of exploration, not to eliminate popularity bias, but to mitigate its negative impact on welfare.
Reinforcement Learning with History-Dependent Dynamic Contexts
We introduce Dynamic Contextual Markov Decision Processes (DCMDPs), a novel reinforcement learning framework for history-dependent environments that generalizes the contextual MDP framework to handle non-Markov environments, where contexts change over time.
Reinforcement Learning with a Terminator
We learn the parameters of the TerMDP and leverage the structure of the estimation problem to provide state-wise confidence bounds.
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
We analyze the limitations of learning from such data with and without external reward, and propose an adjustment of standard imitation learning algorithms to fit this setup.
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning
Cooperative multi-agent reinforcement learning (MARL) faces significant scalability issues due to state and action spaces that are exponentially large in the number of agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Maximum Entropy Reinforcement Learning with Mixture Policies
However, using mixture policies in the Maximum Entropy (MaxEnt) framework is not straightforward.
Uncertainty Estimation Using Riemannian Model~Dynamics for Offline Reinforcement Learning
In this work, we combine parametric and nonparametric methods for uncertainty estimation through a novel latent space based metric.
Action Redundancy in Reinforcement Learning
Maximum Entropy (MaxEnt) reinforcement learning is a powerful learning paradigm which seeks to maximize return under entropy regularization.
Bandits with Partially Observable Confounded Data
We construct a linear bandit algorithm that takes advantage of the projected information, and prove regret bounds.
Language is Power: Representing States Using Natural Language in Reinforcement Learning
Recent advances in reinforcement learning have shown its potential to tackle complex real-life tasks.
Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning
In recent years, there has been significant progress in applying deep reinforcement learning (RL) for solving challenging problems across a wide variety of domains.
Off-Policy Evaluation in Partially Observable Environments
This work studies the problem of batch off-policy evaluation for Reinforcement Learning in partially observable environments.
Distributional Policy Optimization: An Alternative Approach for Continuous Control
We show that optimizing over such sets results in local movement in the action space and thus convergence to sub-optimal solutions.
The Natural Language of Actions
We introduce Act2Vec, a general framework for learning context-based action representation for Reinforcement Learning.
Train on Validation: Squeezing the Data Lemon
We define the notion of on-average-validation-stable algorithms as one in which using small portions of validation data for training does not overfit the model selection process.
The Stochastic Firefighter Problem
We devise a simple policy that only vaccinates neighbors of infected nodes and is optimal on regular trees and on general graphs for a sufficiently large budget.
