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 851–900 of 15113 papers

TitleStatusHype
Active Inference for Stochastic ControlCode1
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learningCode1
Contextualized Rewriting for Text SummarizationCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
Continual Learning with Gated Incremental Memories for sequential data processingCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Evaluating Long-Term Memory in 3D MazesCode1
Evaluating Soccer Player: from Live Camera to Deep Reinforcement LearningCode1
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction EstimationCode1
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement LearningCode1
Evolution Strategies as a Scalable Alternative to Reinforcement LearningCode1
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous ControlCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros StudyCode1
Experience Replay with Likelihood-free Importance WeightsCode1
A Learning System for Motion Planning of Free-Float Dual-Arm Space Manipulator towards Non-Cooperative ObjectCode1
Explainable Reinforcement Learning via a Causal World ModelCode1
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data AugmentationCode1
Exploiting Hybrid Policy in Reinforcement Learning for Interpretable Temporal Logic ManipulationCode1
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
Exploration by Random Network DistillationCode1
Exploration via Elliptical Episodic BonusesCode1
Exploration via Planning for Information about the Optimal TrajectoryCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Constrained Variational Policy Optimization for Safe Reinforcement LearningCode1
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal controlCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Fault-Tolerant Federated Reinforcement Learning with Theoretical GuaranteeCode1
Feasibility Consistent Representation Learning for Safe Reinforcement LearningCode1
Constrained Policy Optimization via Bayesian World ModelsCode1
Constructions in combinatorics via neural networksCode1
Federated Reinforcement Learning with Environment HeterogeneityCode1
Aligning Language Models with Human Preferences via a Bayesian ApproachCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Zero-Shot Reinforcement Learning from Low Quality DataCode1
Finding Effective Security Strategies through Reinforcement Learning and Self-PlayCode1
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive RecommendationCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Conservative Offline Distributional Reinforcement LearningCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space ProgramCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
Adversarial Deep Reinforcement Learning in Portfolio ManagementCode1
Flexible Robust Beamforming for Multibeam Satellite Downlink using Reinforcement LearningCode1
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving PoliciesCode1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
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Benchmark Results

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