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 8101–8125 of 15113 papers

TitleStatusHype
Necessary and Sufficient Oracles: Toward a Computational Taxonomy For Reinforcement Learning—0
Negative Learning Rates and P-Learning—0
Parallel Exploration via Negatively Correlated Search—0
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making—0
Negotiating Team Formation Using Deep Reinforcement Learning—0
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning—0
Neighboring state-based RL Exploration—0
NeoHebbian Synapses to Accelerate Online Training of Neuromorphic Hardware—0
NeoRL: Efficient Exploration for Nonepisodic RL—0
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations—0
Nested Policy Reinforcement Learning for Clinical Decision Support—0
Nested-Wasserstein Self-Imitation Learning for Sequence Generation—0
Network Defense is Not a Game—0
Networked Multi-Agent Reinforcement Learning with Emergent Communication—0
Network Resource Allocation Strategy Based on Deep Reinforcement Learning—0
Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning—0
Network Topology Optimization via Deep Reinforcement Learning—0
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning—0
Neural Algorithms for Graph Navigation—0
Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control—0
Neural architecture impact on identifying temporally extended Reinforcement Learning tasks—0
Neural Architecture Search in Embedding Space—0
Neural-based Control for CubeSat Docking Maneuvers—0
Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection—0
Neural Categorical Priors for Physics-Based Character Control—0
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Benchmark Results

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