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 3621–3630 of 15113 papers

TitleStatusHype
Deep Occupancy-Predictive Representations for Autonomous Driving—0
Graph Decision Transformer—0
A Multiplicative Value Function for Safe and Efficient Reinforcement LearningCode1
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement LearningCode0
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement LearningCode1
Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots—0
Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation—0
Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning—0
adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems—0
Evolutionary Reinforcement Learning: A Survey—0
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Benchmark Results

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