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 10176–10200 of 15113 papers

TitleStatusHype
Option Hedging with Risk Averse Reinforcement Learning—0
Stabilizing Transformer-Based Action Sequence Generation For Q-Learning—0
Towards Safe Policy Improvement for Non-Stationary MDPsCode0
Stochastic Inverse Reinforcement Learning—0
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration—0
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based GamesCode0
Adversarial Attacks on Deep Algorithmic Trading Policies—0
Detecting Rewards Deterioration in Episodic Reinforcement LearningCode0
CoinDICE: Off-Policy Confidence Interval Estimation—0
Error Bounds of Imitating Policies and Environments—0
Incorporating Stylistic Lexical Preferences in Generative Language Models—0
Sample Efficient Reinforcement Learning with REINFORCE—0
What are the Statistical Limits of Offline RL with Linear Function Approximation?—0
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments—0
Optimizing Coverage and Capacity in Cellular Networks using Machine Learning—0
Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning—0
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets—0
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality—0
Safety Verification of Model Based Reinforcement Learning Controllers—0
Logistic Q-Learning—0
Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles—0
Language Inference with Multi-head Automata through Reinforcement Learning—0
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks—0
Runtime Safety Assurance Using Reinforcement Learning—0
Multi-Radar Tracking Optimization for Collaborative Combat—0
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Benchmark Results

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