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 1471–1480 of 15113 papers

TitleStatusHype
Can we hop in general? A discussion of benchmark selection and design using the Hopper environment—0
Reinforcement Learning for Control of Non-Markovian Cellular Population DynamicsCode0
Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement LearningCode0
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language ModelsCode1
Efficient Reinforcement Learning with Large Language Model Priors—0
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning—0
Offline Hierarchical Reinforcement Learning via Inverse Optimization—0
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare—0
Probabilistic Satisfaction of Temporal Logic Constraints in Reinforcement Learning via Adaptive Policy-Switching—0
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Benchmark Results

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