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 751–800 of 15113 papers

TitleStatusHype
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
Adversarial Policies: Attacking Deep Reinforcement LearningCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Deep Symbolic Superoptimization Without Human KnowledgeCode1
Continuous control with deep reinforcement learningCode1
Delay-Aware Model-Based Reinforcement Learning for Continuous ControlCode1
Active MR k-space Sampling with Reinforcement LearningCode1
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional CurriculumCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
Agent57: Outperforming the Atari Human BenchmarkCode1
Deployment-Efficient Reinforcement Learning via Model-Based Offline OptimizationCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Continual Learning with Gated Incremental Memories for sequential data processingCode1
Agent-Controller Representations: Principled Offline RL with Rich Exogenous InformationCode1
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy OptimizationCode1
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
Continual World: A Robotic Benchmark For Continual Reinforcement LearningCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement LearningCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Contextualized Rewriting for Text SummarizationCode1
Constructions in combinatorics via neural networksCode1
Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory SystemsCode1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
Agent with Warm Start and Active Termination for Plane Localization in 3D UltrasoundCode1
Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D UltrasoundCode1
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
A2C is a special case of PPOCode1
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive RecommendationCode1
Discriminative Particle Filter Reinforcement Learning for Complex Partial ObservationsCode1
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement LearningCode1
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot ManipulationCode1
Constrained Policy Optimization via Bayesian World ModelsCode1
Distilling Reinforcement Learning Tricks for Video GamesCode1
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
Distributional Reinforcement Learning via Moment MatchingCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
DMC-VB: A Benchmark for Representation Learning for Control with Visual DistractorsCode1
DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement LearningCode1
Active Inference for Stochastic ControlCode1
Constrained Variational Policy Optimization for Safe Reinforcement LearningCode1
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Benchmark Results

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