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 226–250 of 15113 papers

TitleStatusHype
Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and PerspectivesCode2
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement LearningCode2
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model DisentanglementCode2
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function OptimizationCode2
VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit AssignmentCode2
Stage-Wise Reward Shaping for Acrobatic Robots: A Constrained Multi-Objective Reinforcement Learning ApproachCode2
Training Language Models to Self-Correct via Reinforcement LearningCode2
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning BenchmarksCode2
NAVIX: Scaling MiniGrid Environments with JAXCode2
A Simulation Benchmark for Autonomous Racing with Large-Scale Human DataCode2
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and ReviewCode2
Gradient Boosting Reinforcement LearningCode2
iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvementCode2
Craftium: An Extensible Framework for Creating Reinforcement Learning EnvironmentsCode2
Efficient World Models with Context-Aware TokenizationCode2
GenRL: Multimodal-foundation world models for generalization in embodied agentsCode2
Direct Multi-Turn Preference Optimization for Language AgentsCode2
MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency TradingCode2
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy OptimizationCode2
Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous controlCode2
Diffusion Actor-Critic with Entropy RegulatorCode2
AGILE: A Novel Reinforcement Learning Framework of LLM AgentsCode2
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Benchmark Results

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