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 3401–3410 of 15113 papers

TitleStatusHype
A Novel Reinforcement Learning Model for Post-Incident Malware Investigations—0
Action abstractions for amortized sampling—0
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning—0
Towards Effective Planning Strategies for Dynamic Opinion NetworksCode0
Reinforcement Learning in Non-Markov Market-Making—0
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents—0
Harnessing Causality in Reinforcement Learning With Bagged Decision Times—0
MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments—0
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach—0
Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning—0
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Benchmark Results

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