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 6376–6400 of 15113 papers

TitleStatusHype
Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach—0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning—0
Detecting and Adapting to Novelty in Games—0
Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study—0
Detecting Deceptive Reviews using Generative Adversarial Networks—0
Detecting Worst-case Corruptions via Loss Landscape Curvature in Deep Reinforcement Learning—0
Deterministic Exploration via Stationary Bellman Error Maximization—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Deterministic Value-Policy Gradients—0
DETERRENT: Detecting Trojans using Reinforcement Learning—0
Developing cooperative policies for multi-stage tasks—0
Developing cooperative policies for multi-stage reinforcement learning tasks—0
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments—0
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning—0
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework—0
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm—0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents—0
Development of collective behavior in newborn artificial agents—0
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation—0
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning—0
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance—0
Dexterous Manipulation through Imitation Learning: A Survey—0
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost—0
DGRO: Enhancing LLM Reasoning via Exploration-Exploitation Control and Reward Variance Management—0
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Benchmark Results

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