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 1291–1300 of 15113 papers

TitleStatusHype
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement LearningCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
Adversarial Deep Reinforcement Learning in Portfolio ManagementCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement LearningCode1
Enhancing RL Safety with Counterfactual LLM ReasoningCode1
Hearts Gym: Learning Reinforcement Learning as a Team EventCode1
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational GraphCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
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Benchmark Results

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