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

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Showing 7401–7450 of 15113 papers

TitleStatusHype
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions—0
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning—0
Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization—0
Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization—0
Finetuning Offline World Models in the Real World—0
Fingerprint Policy Optimisation for Robust Reinforcement Learning—0
Finite Horizon Q-learning: Stability, Convergence, Simulations and an application on Smart Grids—0
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents—0
Finite Sample Analyses for TD(0) with Function Approximation—0
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation—0
Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise—0
Finite Sample Analysis of LSTD with Random Projections and Eligibility Traces—0
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency—0
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes—0
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting—0
Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning—0
Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games—0
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator—0
Final Iteration Convergence Bound of Q-Learning: Switching System Approach—0
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise—0
Finite-Time Analysis of Simultaneous Double Q-learning—0
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness—0
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward—0
Finite-Time Error Bounds for Greedy-GQ—0
FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance—0
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