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 8851–8900 of 15113 papers

TitleStatusHype
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers—0
MASAI: Multi-agent Summative Assessment Improvement for Unsupervised Environment DesignCode0
Tangent Space Least Adaptive Clustering—0
Representation Learning for Out-of-distribution Generalization in Reinforcement Learning—0
Disentangled Predictive Representation for Meta-Reinforcement Learning—0
Density-Based Bonuses on Learned Representations for Reward-Free Exploration in Deep Reinforcement Learning—0
Bellman-consistent Pessimism for Offline Reinforcement Learning—0
Learning Task-Relevant Representations with Selective Contrast for Reinforcement Learning in a Real-World Application—0
Exploration-Driven Representation Learning in Reinforcement Learning—0
Data-Efficient Exploration with Self Play for Atari—0
A new soft computing method for integration of expert's knowledge in reinforcement learn-ing problems—0
Learning to Explore Multiple Environments without Rewards—0
Learning on Abstract Domains: A New Approach for Verifiable Guarantee in Reinforcement Learning—0
Model-free Reinforcement Learning for Branching Markov Decision Processes—0
Taylor Expansion of Discount Factors—0
Safe Reinforcement Learning with Linear Function Approximation—0
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs—0
Offline Reinforcement Learning as Anti-Exploration—0
Automatic Risk Adaptation in Distributional Reinforcement Learning—0
Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning—0
A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning—0
GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning—0
DECORE: Deep Compression with Reinforcement Learning—0
Corruption-Robust Offline Reinforcement Learning—0
Hyperspace Neighbor Penetration Approach to Dynamic Programming for Model-Based Reinforcement Learning Problems with Slowly Changing Variables in A Continuous State Space—0
Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning—0
Learning to Play General-Sum Games Against Multiple Boundedly Rational AgentsCode0
Simplifying Deep Reinforcement Learning via Self-SupervisionCode0
Thompson Sampling with a Mixture Prior—0
Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule LearningCode0
RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation—0
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning—0
Online Learning for Stochastic Shortest Path Model via Posterior Sampling—0
Reinforcement Learning for Industrial Control Network Cyber Security Orchestration—0
Over-the-fiber Digital Predistortion Using Reinforcement Learning—0
Offline Inverse Reinforcement Learning—0
Self-Paced Context Evaluation for Contextual Reinforcement LearningCode0
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning—0
Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents—0
Bayesian Bellman Operators—0
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective—0
Curriculum Design for Teaching via Demonstrations: Theory and ApplicationsCode0
A Deep Value-network Based Approach for Multi-Driver Order Dispatching—0
Don't Get Yourself into Trouble! Risk-aware Decision-Making for Autonomous Vehicles—0
Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement LearningCode0
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning—0
Towards Practical Credit Assignment for Deep Reinforcement Learning—0
Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty—0
RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation—0
Verifiable and Compositional Reinforcement Learning SystemsCode0
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Benchmark Results

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