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 3501–3510 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning, a textbook—0
Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection—0
A physics-informed reinforcement learning approach for the interfacial area transport in two-phase flow—0
Agent based modelling for continuously varying supply chains—0
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning—0
Deep Reinforcement Learning amidst Lifelong Non-Stationarity—0
Deep Reinforcement Learning and Transportation Research: A Comprehensive Review—0
Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management—0
Deep Reinforcement Learning and its Neuroscientific Implications—0
Accelerating the Computation of UCB and Related Indices for Reinforcement Learning—0
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Benchmark Results

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