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 9476–9500 of 15113 papers

TitleStatusHype
IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning—0
Improper Reinforcement Learning with Gradient-based Policy Optimization—0
Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary—0
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees—0
Cooperation and Reputation Dynamics with Reinforcement Learning—0
Developing parsimonious ensembles using predictor diversity within a reinforcement learning frameworkCode0
How RL Agents Behave When Their Actions Are ModifiedCode0
Learning from Demonstrations using Signal Temporal Logic—0
Does the Adam Optimizer Exacerbate Catastrophic Forgetting?Code0
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport—0
ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement LearningCode0
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—0
Model-free Representation Learning and Exploration in Low-rank MDPs—0
Sparse Attention Guided Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning—0
Reinforcement Learning for IoT Security: A Comprehensive Survey—0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning—0
Domain Adversarial Reinforcement Learning—0
A Reinforcement learning method for Optical Thin-Film Design—0
Interactive Learning from Activity DescriptionCode0
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning—0
Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network—0
Modelling Cooperation in Network Games with Spatio-Temporal Complexity—0
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators—0
Q-Value Weighted Regression: Reinforcement Learning with Limited DataCode0
Reinforcement Learning For Data Poisoning on Graph Neural Networks—0
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Benchmark Results

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