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 821–830 of 15113 papers

TitleStatusHype
Multi-Fidelity Policy Gradient Algorithms—0
Can We Optimize Deep RL Policy Weights as Trajectory Modeling?—0
Energy-Weighted Flow Matching for Offline Reinforcement Learning—0
Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A reviewCode0
Data-Efficient Learning from Human Interventions for Mobile Robots—0
Provably Correct Automata Embeddings for Optimal Automata-Conditioned Reinforcement Learning—0
Towards Autonomous Reinforcement Learning for Real-World Robotic Manipulation with Large Language Models—0
Rebalanced Multimodal Learning with Data-aware Unimodal Sampling—0
DreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance—0
Rewarding Doubt: A Reinforcement Learning Approach to Confidence Calibration of Large Language Models—0
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Benchmark Results

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