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 10676–10700 of 15113 papers

TitleStatusHype
Setting up experimental Bell test with reinforcement learning—0
Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning—0
Reward Constrained Interactive Recommendation with Natural Language Feedback—0
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open ProblemsCode1
Multiagent Value Iteration Algorithms in Dynamic Programming and Reinforcement Learning—0
Off-Policy Adversarial Inverse Reinforcement LearningCode1
Multi-agent Reinforcement Learning for Decentralized Stable Matching—0
Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach—0
Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge—0
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey—0
Exploration in Reinforcement Learning with Deep Covering Options—0
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD—0
Learning the Arrow of Time for Problems in Reinforcement Learning—0
Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning—0
Implementation Matters in Deep RL: A Case Study on PPO and TRPOCode1
Explain Your Move: Understanding Agent Actions Using Focused Feature SaliencyCode0
Deep Symbolic Superoptimization Without Human KnowledgeCode1
Option Discovery using Deep Skill ChainingCode1
Model Based Reinforcement Learning for Atari—0
RaCT: Toward Amortized Ranking-Critical Training For Collaborative FilteringCode1
The Ingredients of Real World Robotic Reinforcement Learning—0
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information—0
Logic and the 2-Simplicial TransformerCode1
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control—0
Model-based reinforcement learning for biological sequence design—0
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Benchmark Results

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