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 7376–7400 of 15113 papers

TitleStatusHype
FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback—0
FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control—0
Fight Fire with Fire: Defending Against Malicious RL Fine-Tuning via Reward Neutralization—0
Fighting Boredom in Recommender Systems with Linear Reinforcement Learning—0
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching—0
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
Final Adaptation Reinforcement Learning for N-Player Games—0
Financial News-Driven LLM Reinforcement Learning for Portfolio Management—0
Financial Trading with Feature Preprocessing and Recurrent Reinforcement Learning—0
Financial Vision Based Reinforcement Learning Trading Strategy—0
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents—0
Finding Efficient Swimming Strategies in a Three Dimensional Chaotic Flow by Reinforcement Learning—0
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning—0
Finding It at Another Side: A Viewpoint-Adapted Matching Encoder for Change Captioning—0
Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning—0
Finding Optimal Policy for Queueing Models: New Parameterization—0
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning—0
Finding the best design parameters for optical nanostructures using reinforcement learning—0
Find the Fruit: Designing a Zero-Shot Sim2Real Deep RL Planner for Occlusion Aware Plant Manipulation—0
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning—0
Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings—0
Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning—0
Finer Behavioral Foundation Models via Auto-Regressive Features and Advantage Weighting—0
Reducing Non-Normative Text Generation from Language Models—0
Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps—0
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Benchmark Results

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