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 976–1000 of 15113 papers

TitleStatusHype
Active Exploration for Inverse Reinforcement LearningCode1
Doubly Mild Generalization for Offline Reinforcement LearningCode1
Improving Model-Based Reinforcement Learning with Internal State Representations through Self-SupervisionCode1
Dataset Reset Policy Optimization for RLHFCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Data-Efficient Reinforcement Learning with Self-Predictive RepresentationsCode1
A Multiplicative Value Function for Safe and Efficient Reinforcement LearningCode1
Automatic Data Augmentation for Generalization in Reinforcement LearningCode1
Automatic Truss Design with Reinforcement LearningCode1
An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agentsCode1
Inclined Quadrotor Landing using Deep Reinforcement LearningCode1
In Defense of the Unitary Scalarization for Deep Multi-Task LearningCode1
Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control ProblemCode1
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value FunctionCode1
Debiased Contrastive LearningCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement LearningCode1
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving IntelligenceCode1
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter EfficientCode1
Integrating Saliency Ranking and Reinforcement Learning for Enhanced Object DetectionCode1
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement LearningCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
An Alternative Softmax Operator for Reinforcement LearningCode1
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio MinimizationCode1
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Benchmark Results

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