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 9801–9825 of 15113 papers

TitleStatusHype
Defining Admissible Rewards for High Confidence Policy Evaluation—0
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning—0
Deflated Dynamics Value Iteration—0
DEFT: Diverse Ensembles for Fast Transfer in Reinforcement Learning—0
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback—0
Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning—0
Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning—0
Optimism and Delays in Episodic Reinforcement Learning—0
Delayed Geometric Discounts: An Alternative Criterion for Reinforcement Learning—0
Delayed Reinforcement Learning by Imitation—0
Delays in Reinforcement Learning—0
Delegative Reinforcement Learning: learning to avoid traps with a little help—0
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding—0
Delving into adversarial attacks on deep policies—0
Delving into Macro Placement with Reinforcement Learning—0
Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks—0
Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning—0
Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments—0
Demonstration-Guided Deep Reinforcement Learning of Control Policies for Dexterous Human-Robot Interaction—0
Demonstration-Regularized RL—0
Demystifying Multilingual Chain-of-Thought in Process Reward Modeling—0
Demystify Painting with RL—0
De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning—0
De Novo Molecular Design Enabled by Direct Preference Optimization and Curriculum Learning—0
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations—0
Show:102550
← PrevPage 393 of 605Next →

Benchmark Results

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