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 251–275 of 15113 papers

TitleStatusHype
Reverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement LearningCode2
REBEL: Reinforcement Learning via Regressing Relative RewardsCode2
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Sustainability of Data Center Digital Twins with Reinforcement LearningCode2
EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and BenchmarkingCode2
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path PlanningCode2
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban EnvironmentsCode2
EfficientZero V2: Mastering Discrete and Continuous Control with Limited DataCode2
Curiosity-driven Red-teaming for Large Language ModelsCode2
Feedback Efficient Online Fine-Tuning of Diffusion ModelsCode2
GenNBV: Generalizable Next-Best-View Policy for Active 3D ReconstructionCode2
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsCode2
Foundation Policies with Hilbert RepresentationsCode2
A Critical Evaluation of AI Feedback for Aligning Large Language ModelsCode2
Jack of All Trades, Master of Some, a Multi-Purpose Transformer AgentCode2
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement LearningCode2
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model FeedbackCode2
StepCoder: Improve Code Generation with Reinforcement Learning from Compiler FeedbackCode2
Towards Efficient Exact Optimization of Language Model AlignmentCode2
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk ManagementCode2
True Knowledge Comes from Practice: Aligning LLMs with Embodied Environments via Reinforcement LearningCode2
LLMLight: Large Language Models as Traffic Signal Control AgentsCode2
OpenRL: A Unified Reinforcement Learning FrameworkCode2
Evolving Reservoirs for Meta Reinforcement LearningCode2
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Benchmark Results

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