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 1601–1650 of 15113 papers

TitleStatusHype
Scaling Multi-Agent Reinforcement Learning with Selective Parameter SharingCode1
LTL2Action: Generalizing LTL Instructions for Multi-Task RLCode1
Scalable Bayesian Inverse Reinforcement LearningCode1
Multi-Task Reinforcement Learning with Context-based RepresentationsCode1
Improving Model-Based Reinforcement Learning with Internal State Representations through Self-SupervisionCode1
Domain Adaptation In Reinforcement Learning Via Latent Unified State RepresentationCode1
Risk-Averse Offline Reinforcement LearningCode1
Reverb: A Framework For Experience ReplayCode1
rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching TasksCode1
Continuous-Time Model-Based Reinforcement LearningCode1
RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning WorkloadsCode1
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement LearningCode1
Tactical Optimism and Pessimism for Deep Reinforcement LearningCode1
Explainable Reinforcement Learning for Longitudinal ControlCode1
LongiControl: A Reinforcement Learning Environment for Longitudinal Vehicle ControlCode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement LearningCode1
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
NeoRL: A Near Real-World Benchmark for Offline Reinforcement LearningCode1
Multi-Agent Reinforcement Learning with Temporal Logic SpecificationsCode1
Contextualized Rewriting for Text SummarizationCode1
Learning Synthetic Environments for Reinforcement Learning with Evolution StrategiesCode1
Differentiable Trust Region Layers for Deep Reinforcement LearningCode1
Robust Reinforcement Learning on State Observations with Learned Optimal AdversaryCode1
Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis TreatmentCode1
mt5se: An Open Source Framework for Building Autonomous Trading RobotsCode1
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with TransformersCode1
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning ApproachCode1
Deep Reinforcement Learning for Producing Furniture Layout in Indoor ScenesCode1
Grounding Language to Entities and Dynamics for Generalization in Reinforcement LearningCode1
Deep Reinforcement Learning for Active High Frequency TradingCode1
Hierarchical Reinforcement Learning By Discovering Intrinsic OptionsCode1
Controlling the Risk of Conversational Search via Reinforcement LearningCode1
Evaluating Soccer Player: from Live Camera to Deep Reinforcement LearningCode1
Memory-Augmented Reinforcement Learning for Image-Goal NavigationCode1
Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement LearningCode1
Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental ConditionsCode1
Simulating SQL Injection Vulnerability Exploitation Using Q-Learning Reinforcement Learning AgentsCode1
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading RulesCode1
Evolving Reinforcement Learning AlgorithmsCode1
The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from PixelsCode1
Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement LearningCode1
Reinforcement Learning with Latent FlowCode1
MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal ControlCode1
Multi-Agent Trust Region LearningCode1
Cross-Modal Domain Adaptation for Reinforcement LearningCode1
Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient OptimizationCode1
Model-Based Visual Planning with Self-Supervised Functional DistancesCode1
Reinforcement Learning for Control of ValvesCode1
Augmenting Policy Learning with Routines Discovered from a Single DemonstrationCode1
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Benchmark Results

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