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 4151–4175 of 15113 papers

TitleStatusHype
Horizon-Free Regret for Linear Markov Decision Processes—0
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning—0
Meta-operators for Enabling Parallel Planning Using Deep Reinforcement Learning—0
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning—0
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis—0
TeaMs-RL: Teaching LLMs to Generate Better Instruction Datasets via Reinforcement LearningCode0
Learning to Describe for Predicting Zero-shot Drug-Drug InteractionsCode0
HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback—0
Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter ControlCode0
A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware PerspectiveCode0
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning—0
In-context Exploration-Exploitation for Reinforcement Learning—0
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation—0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts—0
Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning—0
(N,K)-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model—0
RLingua: Improving Reinforcement Learning Sample Efficiency in Robotic Manipulations With Large Language Models—0
Distributional Successor Features Enable Zero-Shot Policy Optimization—0
PEaRL: Personalized Privacy of Human-Centric Systems using Early-Exit Reinforcement Learning—0
Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning—0
Enhancing Classification Performance via Reinforcement Learning for Feature Selection—0
Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques—0
Inverse Design of Photonic Crystal Surface Emitting Lasers is a Sequence Modeling Problem—0
Switching the Loss Reduces the Cost in Batch Reinforcement Learning—0
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Benchmark Results

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