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 10101–10125 of 15113 papers

TitleStatusHype
Domain Adversarial Reinforcement Learning—0
Domain Adversarial Reinforcement Learning for Partial Domain Adaptation—0
Domain Generalization for Robust Model-Based Offline Reinforcement Learning—0
Domain-Independent Optimistic Initialization for Reinforcement Learning—0
Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning—0
Domain Knowledge Integration By Gradient Matching For Sample-Efficient Reinforcement Learning—0
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning—0
Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots—0
Domain Randomization via Entropy Maximization—0
Dominion: A New Frontier for AI Research—0
Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured Multi-Turn Decomposition—0
Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning—0
Don't do it: Safer Reinforcement Learning With Rule-based Guidance—0
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL—0
Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework—0
Don't Get Yourself into Trouble! Risk-aware Decision-Making for Autonomous Vehicles—0
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning—0
Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation—0
DOOM: A Novel Adversarial-DRL-Based Op-Code Level Metamorphic Malware Obfuscator for the Enhancement of IDS—0
DOP: Deep Optimistic Planning with Approximate Value Function Evaluation—0
Do recent advancements in model-based deep reinforcement learning really improve data efficiency?—0
Importance of using appropriate baselines for evaluation of data-efficiency in deep reinforcement learning for Atari—0
Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation—0
Double A3C: Deep Reinforcement Learning on OpenAI Gym Games—0
Double Deep Q Networks for Sensor Management in Space Situational Awareness—0
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Benchmark Results

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