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 1611–1620 of 15113 papers

TitleStatusHype
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement LearningCode1
RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning WorkloadsCode1
Tactical Optimism and Pessimism for Deep Reinforcement LearningCode1
LongiControl: A Reinforcement Learning Environment for Longitudinal Vehicle ControlCode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
Explainable Reinforcement Learning for Longitudinal ControlCode1
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement LearningCode1
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
NeoRL: A Near Real-World Benchmark for Offline Reinforcement LearningCode1
Multi-Agent Reinforcement Learning with Temporal Logic SpecificationsCode1
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Benchmark Results

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