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 4431–4440 of 15113 papers

TitleStatusHype
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms—0
A dynamical clipping approach with task feedback for Proximal Policy OptimizationCode0
Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation—0
Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations—0
KnowGPT: Knowledge Graph based Prompting for Large Language Models—0
Reward Certification for Policy Smoothed Reinforcement LearningCode0
Spreeze: High-Throughput Parallel Reinforcement Learning Framework—0
Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing—0
Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Tasks—0
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Benchmark Results

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