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 1441–1450 of 15113 papers

TitleStatusHype
When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter—0
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving—0
Multi-Objective-Optimization Multi-AUV Assisted Data Collection Framework for IoUT Based on Offline Reinforcement Learning—0
Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets—0
Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning AgentsCode1
Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment—0
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model DisentanglementCode2
Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task—0
Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement LearningCode0
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis—0
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Benchmark Results

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