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 1861–1870 of 15113 papers

TitleStatusHype
Reinforcement learning with combinatorial actions for coupled restless banditsCode1
Reinforcement Learning with Convex ConstraintsCode1
Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock PoolsCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement LearningCode1
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Reinforcement Learning with Prototypical RepresentationsCode1
Reinforcement Learning with Sparse Rewards using Guidance from Offline DemonstrationCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
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Benchmark Results

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