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 1801–1810 of 15113 papers

TitleStatusHype
qgym: A Gym for Training and Benchmarking RL-Based Quantum CompilationCode1
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Automatic Truss Design with Reinforcement LearningCode1
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation ErrorsCode1
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code SynthesisCode1
Quantifying Generalization in Reinforcement LearningCode1
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy SearchCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
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