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 4391–4400 of 15113 papers

TitleStatusHype
Learning Online Policies for Person Tracking in Multi-View Environments—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
Agent based modelling for continuously varying supply chains—0
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization—0
Gradient Shaping for Multi-Constraint Safe Reinforcement Learning—0
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning—0
Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic—0
Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling—0
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning—0
REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback—0
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Benchmark Results

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