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 4026–4050 of 15113 papers

TitleStatusHype
The Natural Language of ActionsCode0
Modular Deep Reinforcement Learning with Temporal Logic SpecificationsCode0
Random Projection in Neural Episodic ControlCode0
Risk-Aware Active Inverse Reinforcement LearningCode0
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial OptimizationCode0
Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous DrivingCode0
The Option-Critic ArchitectureCode0
Better-than-Demonstrator Imitation Learning via Automatically-Ranked DemonstrationsCode0
Ranking Policy DecisionsCode0
Ranking Policy GradientCode0
Ranking Sentences for Extractive Summarization with Reinforcement LearningCode0
Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with ApplicationsCode0
Offline RL with Smooth OOD Generalization in Convex Hull and its NeighborhoodCode0
Theory of Mind for Deep Reinforcement Learning in HanabiCode0
Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement LearningCode0
Risk-sensitive control as inference with Rényi divergenceCode0
Park: An Open Platform for Learning-Augmented Computer SystemsCode0
The PlayStation Reinforcement Learning Environment (PSXLE)Code0
The Potential of the Return Distribution for Exploration in RLCode0
Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric MethodsCode0
Offline Safe Reinforcement Learning Using Trajectory ClassificationCode0
The Pump Scheduling Problem: A Real-World Scenario for Reinforcement LearningCode0
Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR StreamingCode0
Off-Policy Actor-CriticCode0
Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy EvaluationCode0
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Benchmark Results

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