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 8476–8500 of 15113 papers

TitleStatusHype
Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning—0
Pre-training of Deep RL Agents for Improved Learning under Domain Randomization—0
Pretraining & Reinforcement Learning: Sharpening the Axe Before Cutting the Tree—0
Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning—0
Preventing Imitation Learning with Adversarial Policy Ensembles—0
Preventive Energy Management for Distribution Systems Under Uncertain Events: A Deep Reinforcement Learning Approach—0
PRewrite: Prompt Rewriting with Reinforcement Learning—0
Pricing commodity swing options—0
Primal-Dual Spectral Representation for Off-policy Evaluation—0
Primitive Agentic First-Order Optimization—0
Primitive Skill-based Robot Learning from Human Evaluative Feedback—0
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF—0
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons—0
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation—0
Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration—0
Prioritized offline Goal-swapping Experience Replay—0
Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays—0
Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning—0
Prior Preference Learning From Experts: Designing A Reward with Active Inference—0
Prior Preference Learning from Experts:Designing a Reward with Active Inference—0
Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning—0
PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations—0
Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning—0
Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning—0
Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning—0
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Benchmark Results

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