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

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Showing 6251–6275 of 15113 papers

TitleStatusHype
Reinforcement Learning with Sparse Rewards using Guidance from Offline DemonstrationCode1
Scenario-Assisted Deep Reinforcement Learning—0
Bayesian Nonparametrics for Offline Skill DiscoveryCode0
A Reinforcement Learning Approach to Domain-Knowledge Inclusion Using Grammar Guided Symbolic RegressionCode0
Intelligent Autonomous Intersection Management—0
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
GrASP: Gradient-Based Affordance Selection for Planning—0
Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks—0
Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park—0
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence—0
PolicyCleanse: Backdoor Detection and Mitigation in Reinforcement Learning—0
Approximating Gradients for Differentiable Quality Diversity in Reinforcement LearningCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Local Explanations for Reinforcement Learning—0
Provable Reinforcement Learning with a Short-Term Memory—0
skrl: Modular and Flexible Library for Reinforcement Learning—0
Optimizing Warfarin Dosing using Deep Reinforcement LearningCode0
Reward-Respecting Subtasks for Model-Based Reinforcement Learning—0
Policy Optimization for Stochastic Shortest Path—0
Model-Based Offline Meta-Reinforcement Learning with Regularization—0
Attacking c-MARL More Effectively: A Data Driven Approach—0
Geometric Multimodal Contrastive Representation LearningCode1
Stochastic Gradient Descent with Dependent Data for Offline Reinforcement Learning—0
Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning—0
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill DiversityCode1
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Benchmark Results

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