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 7826–7850 of 15113 papers

TitleStatusHype
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