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 7426–7450 of 15113 papers

TitleStatusHype
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance—0
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations—0
First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation—0
First-Order Problem Solving through Neural MCTS based Reinforcement Learning—0
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach—0
First-Person Activity Forecasting with Online Inverse Reinforcement Learning—0
First-spike based visual categorization using reward-modulated STDP—0
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)—0
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis—0
FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control—0
Fitted Q-iteration in continuous action-space MDPs—0
Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have—0
FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism—0
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning—0
Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS—0
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs—0
FLAME: Factuality-Aware Alignment for Large Language Models—0
FLAM: Foundation Model-Based Body Stabilization for Humanoid Locomotion and Manipulation—0
FlashRL: A Reinforcement Learning Platform for Flash Games—0
Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning—0
Flatland-RL : Multi-Agent Reinforcement Learning on Trains—0
FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation—0
Flexible and Efficient Long-Range Planning Through Curious Exploration—0
Flexible Blood Glucose Control: Offline Reinforcement Learning from Human Feedback—0
Flexible Multiple-Objective Reinforcement Learning for Chip Placement—0
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Benchmark Results

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