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 7626–7650 of 15113 papers

TitleStatusHype
Improving the Efficiency of Off-Policy Reinforcement Learning by Accounting for Past Decisions—0
Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information—0
Graph augmented Deep Reinforcement Learning in the GameRLand3D environment—0
Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving EnvironmentCode0
Alpha-Mini: Minichess Agent with Deep Reinforcement LearningCode0
Newsvendor Model with Deep Reinforcement LearningCode0
Off Environment Evaluation Using Convex Risk MinimizationCode0
Soft Actor-Critic with Cross-Entropy Policy OptimizationCode0
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee—0
Reinforcement Learning based Sequential Batch-sampling for Bayesian Optimal Experimental Design—0
Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach—0
District Cooling System Control for Providing Operating Reserve based on Safe Deep Reinforcement Learning—0
A Scalable Deep Reinforcement Learning Model for Online Scheduling Coflows of Multi-Stage Jobs for High Performance Computing—0
Do Androids Dream of Electric Fences? Safety-Aware Reinforcement Learning with Latent Shielding—0
A deep reinforcement learning model for predictive maintenance planning of road assets: Integrating LCA and LCCA—0
AGPNet -- Autonomous Grading Policy Network—0
Differentially Private Regret Minimization in Episodic Markov Decision ProcessesCode0
Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions—0
Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units—0
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning—0
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment—0
Masked Deep Q-Recommender for Effective Question Scheduling—0
Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex models—0
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning—0
Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading—0
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Benchmark Results

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