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 7001–7025 of 15113 papers

TitleStatusHype
Universal Agent Mixtures and the Geometry of Intelligence—0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning—0
Universal Learning Waveform Selection Strategies for Adaptive Target Tracking—0
Universal Successor Features Based Deep Reinforcement Learning for Navigation—0
Universal Successor Features for Transfer Reinforcement Learning—0
Universal Successor Representations for Transfer Reinforcement Learning—0
Universal Trading for Order Execution with Oracle Policy Distillation—0
UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning—0
UniZero: Generalized and Efficient Planning with Scalable Latent World Models—0
Unlearning Works Better Than You Think: Local Reinforcement-Based Selection of Auxiliary Objectives—0
Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem—0
Unlocking Pixels for Reinforcement Learning via Implicit Attention—0
Unlocking the Potential of Simulators: Design with RL in Mind—0
Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models—0
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity—0
Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software—0
Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation—0
UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI—0
Unsupervised Active Pre-Training for Reinforcement Learning—0
Unsupervised Basis Function Adaptation for Reinforcement Learning—0
Unsupervised Basis Function Adaptation for Reinforcement Learning—0
Unsupervised Behavior Extraction via Random Intent Priors—0
Unsupervised Compressive Text Summarisation with Reinforcement Learning—0
Unsupervised Context Rewriting for Open Domain Conversation—0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
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Benchmark Results

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