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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5751–5775 of 15113 papers

TitleStatusHype
Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning—0
Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework—0
Learning and Understanding a Disentangled Feature Representation for Hidden Parameters in Reinforcement Learning—0
Approximating Martingale Process for Variance Reduction in Deep Reinforcement Learning with Large State Space—0
Behavior Estimation from Multi-Source Data for Offline Reinforcement LearningCode0
Autotuning PID control using Actor-Critic Deep Reinforcement Learning—0
Offline Policy Evaluation and Optimization under Confounding—0
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators—0
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration—0
Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representations—0
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes—0
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning—0
Tackling Visual Control via Multi-View Exploration Maximization—0
Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies—0
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning—0
Causal Deep Reinforcement Learning Using Observational Data—0
Continuous Episodic Control—0
AcceRL: Policy Acceleration Framework for Deep Reinforcement Learning—0
Is Conditional Generative Modeling all you need for Decision-Making?—0
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning—0
Hypernetworks for Zero-shot Transfer in Reinforcement Learning—0
Applying Deep Reinforcement Learning to the HP Model for Protein Structure PredictionCode0
Domain Generalization for Robust Model-Based Offline Reinforcement Learning—0
Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization—0
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Benchmark Results

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