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 851–875 of 15113 papers

TitleStatusHype
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous DrivingCode1
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement LearningCode1
Entropy-Regularized Process Reward ModelCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics TasksCode1
Conservative Offline Distributional Reinforcement LearningCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
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
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Evaluating Long-Term Memory in 3D MazesCode1
Evolution Strategies as a Scalable Alternative to Reinforcement LearningCode1
Maximum Mutation Reinforcement Learning for Scalable ControlCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
A Learning System for Motion Planning of Free-Float Dual-Arm Space Manipulator towards Non-Cooperative ObjectCode1
Execution-based Code Generation using Deep Reinforcement LearningCode1
Experience Replay with Likelihood-free Importance WeightsCode1
Expert-Supervised Reinforcement Learning for Offline Policy Learning and EvaluationCode1
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
An Alternative Softmax Operator for Reinforcement LearningCode1
Exploiting Hybrid Policy in Reinforcement Learning for Interpretable Temporal Logic ManipulationCode1
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine TranslationCode1
Zero-Shot Reinforcement Learning from Low Quality DataCode1
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Benchmark Results

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