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 1111–1120 of 15113 papers

TitleStatusHype
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Efficient Continuous Control with Double Actors and Regularized CriticsCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
Efficient Diffusion Policies for Offline Reinforcement LearningCode1
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
Action Branching Architectures for Deep Reinforcement LearningCode1
Effective Diversity in Population Based Reinforcement LearningCode1
A Deep Reinforcement Learning Framework for the Financial Portfolio Management ProblemCode1
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Benchmark Results

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