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 5501–5525 of 15113 papers

TitleStatusHype
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop—0
Interactive Reinforcement Learning for Object Grounding via Self-Talking—0
Interactive Reinforcement Learning for Table Balancing Robot—0
Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human/Agent's Demonstration—0
Interactive Search Based on Deep Reinforcement Learning—0
Interactive Spoken Content Retrieval by Deep Reinforcement Learning—0
Interactive Teaching Algorithms for Inverse Reinforcement Learning—0
Interactive Video Corpus Moment Retrieval using Reinforcement Learning—0
Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning—0
Interleaved Reasoning for Large Language Models via Reinforcement Learning—0
Internal Model from Observations for Reward Shaping—0
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning—0
Interpretability via Model Extraction—0
Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection—0
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems—0
Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction—0
Interpretable Control by Reinforcement Learning—0
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information—0
Interpretable Disease Prediction based on Reinforcement Path Reasoning over Knowledge Graphs—0
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning—0
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents—0
Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines—0
Interpretable Meta-Reinforcement Learning with Actor-Critic Method—0
Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming—0
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration—0
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Benchmark Results

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