SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5526–5550 of 15113 papers

TitleStatusHype
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic ForgettingCode1
Predecessor Features—0
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation—0
On Gap-dependent Bounds for Offline Reinforcement Learning—0
The Phenomenon of Policy Churn—0
Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL—0
DM^2: Decentralized Multi-Agent Reinforcement Learning for Distribution MatchingCode0
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
Byzantine-Robust Online and Offline Distributed Reinforcement Learning—0
IGLU Gridworld: Simple and Fast Environment for Embodied Dialog AgentsCode1
A Mixture-of-Expert Approach to RL-based Dialogue Management—0
Human-AI Shared Control via Policy DissectionCode2
Robust Longitudinal Control for Vehicular Autonomous Platoons Using Deep Reinforcement Learning—0
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game—0
Provable General Function Class Representation Learning in Multitask Bandits and MDPs—0
Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints—0
Multi-Agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP—0
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning—0
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems—0
k-Means Maximum Entropy Exploration—0
Graph Backup: Data Efficient Backup Exploiting Markovian TransitionsCode0
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning—0
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the CloudCode0
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
A Simulation Environment and Reinforcement Learning Method for Waste Reduction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified