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 701–725 of 15113 papers

TitleStatusHype
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman ProblemsCode1
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route PredictionCode1
Submodular Reinforcement LearningCode1
Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal ControlCode1
HIQL: Offline Goal-Conditioned RL with Latent States as ActionsCode1
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value RegularizationCode1
JoinGym: An Efficient Query Optimization Environment for Reinforcement LearningCode1
Explaining Autonomous Driving Actions with Visual Question AnsweringCode1
PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop GamesCode1
Natural Actor-Critic for Robust Reinforcement Learning with Function ApproximationCode1
SafeDreamer: Safe Reinforcement Learning with World ModelsCode1
Robotic Manipulation Datasets for Offline Compositional Reinforcement LearningCode1
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control TasksCode1
Payload-Independent Direct Cost Learning for Image SteganographyCode1
RLTF: Reinforcement Learning from Unit Test FeedbackCode1
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive RecommendationCode1
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive LearningCode1
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learningCode1
Model-Bellman Inconsistency for Model-based Offline Reinforcement LearningCode1
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand CoresCode1
MRHER: Model-based Relay Hindsight Experience Replay for Sequential Object Manipulation Tasks with Sparse RewardsCode1
Automatic Truss Design with Reinforcement LearningCode1
Learning to Modulate pre-trained Models in RLCode1
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory WeightingCode1
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Benchmark Results

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