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 46214630 of 15113 papers

TitleStatusHype
Multi-agent Dynamic Algorithm ConfigurationCode1
Sustainable Online Reinforcement Learning for Auto-biddingCode1
Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement LearningCode1
Observed Adversaries in Deep Reinforcement Learning0
Output Feedback Adaptive Optimal Control of Affine Nonlinear systems with a Linear Measurement Model0
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics BeliefCode0
A Mixture of Surprises for Unsupervised Reinforcement LearningCode1
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning0
Dissipative residual layers for unsupervised implicit parameterization of data manifolds0
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning0
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Benchmark Results

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