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 10151–10200 of 15113 papers

TitleStatusHype
Understanding the Pathologies of Approximate Policy Evaluation when Combined with Greedification in Reinforcement Learning—0
Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids—0
Can Reinforcement Learning for Continuous Control Generalize Across Physics Engines?—0
Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient—0
Conservative Safety Critics for Exploration—0
Learning to be Safe: Deep RL with a Safety Critic—0
Affordance as general value function: A computational model—0
Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications—0
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational SearchingCode0
Behavior Priors for Efficient Reinforcement Learning—0
Forethought and Hindsight in Credit Assignment—0
Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network—0
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards—0
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills—0
VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning—0
Pairwise heuristic sequence alignment algorithm based on deep reinforcement learning—0
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning—0
Lyapunov-Based Reinforcement Learning State Estimator—0
Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision—0
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things—0
How to Make Deep RL Work in PracticeCode0
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control—0
Learning to Deceive Knowledge Graph Augmented Models via Targeted PerturbationCode0
Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search—0
Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning—0
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