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 5476–5500 of 15113 papers

TitleStatusHype
Goal-Space Planning with Subgoal Models—0
RORL: Robust Offline Reinforcement Learning via Conservative SmoothingCode1
Neuro-Nav: A Library for Neurally-Plausible Reinforcement LearningCode2
Specification-Guided Learning of Nash Equilibria with High Social Welfare—0
Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning—0
Markovian Interference in Experiments—0
Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation—0
Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path—0
Effects of Safety State Augmentation on Safe Exploration—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review—0
Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL—0
DDPG based on multi-scale strokes for financial time series trading strategy—0
ARC -- Actor Residual Critic for Adversarial Imitation Learning—0
Learning Dynamics and Generalization in Reinforcement Learning—0
Rapid Learning of Spatial Representations for Goal-Directed Navigation Based on a Novel Model of Hippocampal Place Fields—0
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning—0
MACC: Cross-Layer Multi-Agent Congestion Control with Deep Reinforcement Learning—0
Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning—0
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning—0
Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL—0
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning—0
Adaptive Tree Backup Algorithms for Temporal-Difference Reinforcement Learning—0
A Deep Reinforcement Learning Framework For Column GenerationCode0
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Benchmark Results

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