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 351–375 of 15113 papers

TitleStatusHype
Assessment of Reinforcement Learning for Macro PlacementCode2
A Simulation Benchmark for Autonomous Racing with Large-Scale Human DataCode2
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement LearningCode2
Deep Reinforcement Learning for Multi-Agent InteractionCode2
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics ModelsCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with TransformersCode2
Model-agnostic and Scalable Counterfactual Explanations via Reinforcement LearningCode2
D4RL: Datasets for Deep Data-Driven Reinforcement LearningCode2
CaRL: Learning Scalable Planning Policies with Simple RewardsCode2
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
DayDreamer: World Models for Physical Robot LearningCode2
GenRL: Multimodal-foundation world models for generalization in embodied agentsCode2
AMP: Adversarial Motion Priors for Stylized Physics-Based Character ControlCode2
Curiosity-driven Red-teaming for Large Language ModelsCode2
Neuroevolution of Self-Interpretable AgentsCode2
NoisyRollout: Reinforcing Visual Reasoning with Data AugmentationCode2
Datasets and Benchmarks for Offline Safe Reinforcement LearningCode2
Decoupling Representation Learning from Reinforcement LearningCode2
Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement LearningCode2
Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System CollaborationCode2
Craftium: An Extensible Framework for Creating Reinforcement Learning EnvironmentsCode2
Benchmarking Deep Reinforcement Learning for Continuous ControlCode2
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Benchmark Results

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