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 1351–1375 of 15113 papers

TitleStatusHype
Exploration in Approximate Hyper-State Space for Meta Reinforcement LearningCode1
Interferobot: aligning an optical interferometer by a reinforcement learning agentCode1
Interpretable Concept Bottlenecks to Align Reinforcement Learning AgentsCode1
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement LearningCode1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data FormatCode1
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable EnvironmentCode1
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model CheckingCode1
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement LearningCode1
Zero-Shot Compositional Policy Learning via Language GroundingCode1
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
Adversarial Soft Advantage Fitting: Imitation Learning without Policy OptimizationCode1
CoRL: Environment Creation and Management Focused on System IntegrationCode1
BabyAI 1.1Code1
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine TranslationCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
A Reinforcement Learning Benchmark for Autonomous Driving in Intersection ScenariosCode1
Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw PuzzlesCode1
JoinGym: An Efficient Query Optimization Environment for Reinforcement LearningCode1
A Workflow for Offline Model-Free Robotic Reinforcement LearningCode1
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion PlanningCode1
Exploration via Elliptical Episodic BonusesCode1
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Benchmark Results

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