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 7801–7850 of 15113 papers

TitleStatusHype
Safe Exploration by Solving Early Terminated MDP—0
Policy Gradient Methods for Distortion Risk Measures—0
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGymCode1
Inferring Probabilistic Reward Machines from Non-Markovian Reward Processes for Reinforcement Learning—0
Aligning an optical interferometer with beam divergence control and continuous action spaceCode0
Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios—0
Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention—0
Offline reinforcement learning with uncertainty for treatment strategies in sepsis—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy LearningCode0
CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks—0
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection—0
Adaptive Stress Testing for Adversarial Learning in a Financial Environment—0
Adaptation of Quadruped Robot Locomotion with Meta-Learning—0
Enhancing Video Analytics Accuracy via Real-time Automated Camera Parameter Tuning—0
Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal TransformersCode1
Sublinear Regret for Learning POMDPs—0
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning—0
Offline Meta-Reinforcement Learning with Online Self-SupervisionCode1
Federated Model Search via Reinforcement Learning—0
Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning—0
DORA: Toward Policy Optimization for Task-oriented Dialogue System with Efficient ContextCode0
Quadruped Locomotion on Non-Rigid Terrain using Reinforcement Learning—0
Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning—0
Distributed Online Service Coordination Using Deep Reinforcement LearningCode1
THE SJTU SYSTEM FOR DCASE2021 CHALLENGE TASK 6: AUDIO CAPTIONING BASED ON ENCODER PRE-TRAINING AND REINFORCEMENT LEARNINGCode1
Meta-Reinforcement Learning for Heuristic Planning—0
Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement LearningCode1
A Unified Off-Policy Evaluation Approach for General Value Function—0
A Short Note on the Relationship of Information Gain and Eluder Dimension—0
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement LearningCode1
Gradient Importance Learning for Incomplete ObservationsCode0
Control of rough terrain vehicles using deep reinforcement learning—0
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement LearningCode0
A Review of Explainable Artificial Intelligence in Manufacturing—0
Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory SystemsCode1
The Least Restriction for Offline Reinforcement Learning—0
Winning at Any Cost -- Infringing the Cartel Prohibition With Reinforcement Learning—0
Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and ExploitationCode1
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces—0
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics—0
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning—0
Traffic Signal Control with Communicative Deep Reinforcement Learning Agents: a Case Study—0
Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement LearningCode0
Mava: a research library for distributed multi-agent reinforcement learning in JAXCode1
Examining average and discounted reward optimality criteria in reinforcement learning—0
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning—0
Controlled Interacting Particle Algorithms for Simulation-based Reinforcement LearningCode0
RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensingCode0
Reinforcement Learning for Feedback-Enabled Cyber Resilience—0
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Benchmark Results

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