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 11651–11700 of 15113 papers

TitleStatusHype
Learning to Predict Without Looking Ahead: World Models Without Forward PredictionCode2
Constrained Reinforcement Learning Has Zero Duality Gap—0
Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning—0
Certified Adversarial Robustness for Deep Reinforcement Learning—0
Asynchronous Methods for Model-Based Reinforcement LearningCode0
Entity Abstraction in Visual Model-Based Reinforcement LearningCode0
Quantum enhancements for deep reinforcement learning in large spacesCode0
Learning Data Manipulation for Augmentation and WeightingCode1
Generalization in Reinforcement Learning with Selective Noise Injection and Information BottleneckCode0
Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control—0
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning—0
Minimax Weight and Q-Function Learning for Off-Policy Evaluation—0
Task-Oriented Language Grounding for Language Input with Multiple Sub-Goals of Non-Linear OrderCode0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement LearningCode0
Reinforcement Learning-Enabled Reliable Wireless Sensor Networks in Dynamic Underground Environments—0
ZPD Teaching Strategies for Deep Reinforcement Learning from DemonstrationsCode0
Convergent Policy Optimization for Safe Reinforcement LearningCode0
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding—0
On the convergence of projective-simulation-based reinforcement learning in Markov decision processes—0
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement LearningCode0
Deep Reinforcement Learning for Synthesizing Functions in Higher-Order LogicCode0
Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller—0
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile ManipulatorsCode0
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement LearningCode0
Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting BehaviorCode0
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement LearningCode2
Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics—0
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition—0
Contextual Imagined Goals for Self-Supervised Robotic LearningCode0
Attention-based Curiosity-driven Exploration in Deep Reinforcement LearningCode0
Efficient Decoupled Neural Architecture Search by Structure and Operation SamplingCode0
Optimizing Percentile Criterion Using Robust MDPs—0
Learning Q-network for Active Information AcquisitionCode1
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles—0
Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation—0
Robust Visual Domain Randomization for Reinforcement LearningCode0
Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial CriticsCode0
State2vec: Off-Policy Successor Features Approximators—0
Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning—0
Application of Reinforcement Learning for 5G Scheduling Parameter Optimization—0
IPO: Interior-point Policy Optimization under Constraints—0
Combining Benefits from Trajectory Optimization and Deep Reinforcement Learning—0
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated FlightCode1
Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach—0
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation—0
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination—0
Policy Optimization for H_2 Linear Control with H_ Robustness Guarantee: Implicit Regularization and Global Convergence—0
Regularization Matters in Policy OptimizationCode0
Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer—0
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Benchmark Results

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