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 2821–2830 of 15113 papers

TitleStatusHype
A bandit approach to curriculum generation for automatic speech recognition—0
Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games—0
Adaptive Dialog Policy Learning with Hindsight and User Modeling—0
A Unifying View of Optimism in Episodic Reinforcement Learning—0
ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning—0
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning—0
A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems—0
A unified view of entropy-regularized Markov decision processes—0
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Benchmark Results

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