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 4081–4090 of 15113 papers

TitleStatusHype
Don't do it: Safer Reinforcement Learning With Rule-based Guidance—0
Lexicographic Multi-Objective Reinforcement LearningCode1
Towards Learning Abstractions via Reinforcement Learning—0
Representation Learning in Deep RL via Discrete Information Bottleneck—0
Offline Reinforcement Learning via Linear-Programming with Error-Bound Induced Constraints—0
On Pathologies in KL-Regularized Reinforcement Learning from Expert DemonstrationsCode1
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation—0
Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach—0
Strangeness-driven Exploration in Multi-Agent Reinforcement LearningCode0
Data-driven control of COVID-19 in buildings: a reinforcement-learning approach—0
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Benchmark Results

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