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 2411–2420 of 15113 papers

TitleStatusHype
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization—0
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping—0
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning—0
A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications—0
Balancing Two-Player Stochastic Games with Soft Q-Learning—0
Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols—0
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control—0
Balancing SoC in Battery Cells using Safe Action Perturbations—0
Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval—0
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization—0
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Benchmark Results

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