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

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Showing 4271–4280 of 15113 papers

TitleStatusHype
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators—0
The Effectiveness of World Models for Continual Reinforcement LearningCode1
Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning—0
Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies—0
Causal Deep Reinforcement Learning Using Observational Data—0
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
Improved Representation of Asymmetrical Distances with Interval Quasimetric EmbeddingsCode1
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning—0
Hypernetworks for Zero-shot Transfer in Reinforcement Learning—0
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes—0
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Benchmark Results

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