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 4591–4600 of 15113 papers

TitleStatusHype
Iteratively Learn Diverse Strategies with State Distance Information—0
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes—0
SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models—0
Using Experience Classification for Training Non-Markovian Tasks—0
On The Expressivity of Objective-Specification Formalisms in Reinforcement Learning—0
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning—0
Improving Generalization of Alignment with Human Preferences through Group Invariant Learning—0
Learning to Optimise Climate Sensor Placement using a Transformer—0
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning—0
Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement LearningCode0
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Benchmark Results

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