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

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Showing 1076–1100 of 15113 papers

TitleStatusHype
RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture SearchCode1
Reinforcement Learning-based Placement of Charging Stations in Urban Road NetworksCode1
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum GamesCode1
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement LearningCode1
Mildly Conservative Q-Learning for Offline Reinforcement LearningCode1
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-RiskCode1
Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement LearningCode1
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement LearningCode1
Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with TransformerCode1
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage RegressionCode1
RORL: Robust Offline Reinforcement Learning via Conservative SmoothingCode1
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate ProgressCode1
When does return-conditioned supervised learning work for offline reinforcement learning?Code1
Deep Transformer Q-Networks for Partially Observable Reinforcement LearningCode1
NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic RegularizationCode1
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic ForgettingCode1
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual ActorCode1
IGLU Gridworld: Simple and Fast Environment for Embodied Dialog AgentsCode1
RLx2: Training a Sparse Deep Reinforcement Learning Model from ScratchCode1
On the Robustness of Safe Reinforcement Learning under Observational PerturbationsCode1
Provable Benefits of Representational Transfer in Reinforcement LearningCode1
Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective TrajectoriesCode1
Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming ChallengesCode1
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient ExplorationCode1
FedFormer: Contextual Federation with Attention in Reinforcement LearningCode1
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Benchmark Results

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