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 1171–1180 of 15113 papers

TitleStatusHype
Adaptive Reward Design for Reinforcement LearningCode0
Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement—0
Continuous-time optimal investment with portfolio constraints: a reinforcement learning approach—0
Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection—0
Reward Machine Inference for Robotic Manipulation—0
Physics Instrument Design with Reinforcement Learning—0
PickLLM: Context-Aware RL-Assisted Large Language Model Routing—0
From Text to Trajectory: Exploring Complex Constraint Representation and Decomposition in Safe Reinforcement Learning—0
Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer—0
Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning—0
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Benchmark Results

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