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 3911–3920 of 15113 papers

TitleStatusHype
Delegative Reinforcement Learning: learning to avoid traps with a little help—0
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning—0
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding—0
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping—0
Delving into adversarial attacks on deep policies—0
Delving into Macro Placement with Reinforcement Learning—0
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization—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
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation—0
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Benchmark Results

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