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 10076–10100 of 15113 papers

TitleStatusHype
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning—0
Divide-and-Conquer Monte Carlo Tree Search—0
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games—0
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning—0
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation—0
DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV—0
dm_control: Software and Tasks for Continuous Control—0
DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks—0
Do Androids Dream of Electric Fences? Safety-Aware Reinforcement Learning with Latent Shielding—0
Do Artificial Reinforcement-Learning Agents Matter Morally?—0
Do as I can, not as I get—0
Do Autonomous Agents Benefit from Hearing?—0
DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances—0
Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI—0
Do Deep Reinforcement Learning Algorithms really Learn to Navigate?—0
Does Explicit Prediction Matter in Deep Reinforcement Learning-Based Energy Management?—0
How Does an Approximate Model Help in Reinforcement Learning?—0
Does Sparsity Help in Learning Misspecified Linear Bandits?—0
Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games—0
Domain Adaptation for Offline Reinforcement Learning with Limited Samples—0
Domain Adaptation for Reinforcement Learning on the Atari—0
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition—0
Domain Adaptive Fake News Detection via Reinforcement Learning—0
Show:102550
← PrevPage 404 of 605Next →

Benchmark Results

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