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

Showing 42764300 of 15113 papers

TitleStatusHype
TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithmCode0
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement LearningCode0
Online Baum-Welch algorithm for Hierarchical Imitation LearningCode0
Trial without Error: Towards Safe Reinforcement Learning via Human InterventionCode0
Safe Exploration Method for Reinforcement Learning under Existence of DisturbanceCode0
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their EnvironmentsCode0
TrojDRL: Trojan Attacks on Deep Reinforcement Learning AgentsCode0
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic SpacesCode0
Reinforcement and Imitation Learning for Diverse Visuomotor SkillsCode0
Meta-Reinforcement Learning by Tracking Task Non-stationarityCode0
Trust, but verify: model-based exploration in sparse reward environmentsCode0
SafeLife 1.0: Exploring Side Effects in Complex EnvironmentsCode0
Trust Region-Guided Proximal Policy OptimizationCode0
Trust-Region Twisted Policy ImprovementCode0
SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal ControlCode0
Constrained Reinforcement Learning using Distributional Representation for Trustworthy Quadrotor UAV Tracking ControlCode0
Safe Model-based Reinforcement Learning with Stability GuaranteesCode0
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning ApproachCode0
Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement LearningCode0
Reinforcement Knowledge Graph Reasoning for Explainable RecommendationCode0
PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social DilemmasCode0
Reinforcement LearningCode0
Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence ModelCode0
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of ChaosCode0
Constrained Policy Improvement for Safe and Efficient Reinforcement LearningCode0
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Benchmark Results

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