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 4451–4475 of 15113 papers

TitleStatusHype
Knowing the Past to Predict the Future: Reinforcement Virtual Learning—0
Multi-Agent Reinforcement Learning for Adaptive Mesh RefinementCode1
Reinforcement Learning Applied to Trading Systems: A Survey—0
Reinforcement Learning in Education: A Multi-Armed Bandit Approach—0
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian—0
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement LearningCode1
Event Tables for Efficient Experience Replay—0
Can maker-taker fees prevent algorithmic cooperation in market making?Code0
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning—0
Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language InstructionsCode0
Agent-Controller Representations: Principled Offline RL with Rich Exogenous InformationCode1
Disentangled (Un)Controllable FeaturesCode0
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment TreesCode1
Teacher-student curriculum learning for reinforcement learning—0
Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement LearningCode0
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning—0
DanZero: Mastering GuanDan Game with Reinforcement Learning—0
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games—0
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning—0
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction—0
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification AlgorithmsCode1
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning—0
BIMRL: Brain Inspired Meta Reinforcement LearningCode1
DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous DrivingCode1
Reinforcement Learning-based Defect Mitigation for Quality Assurance of Additive Manufacturing—0
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Benchmark Results

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