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 1751–1760 of 15113 papers

TitleStatusHype
Option-Aware Adversarial Inverse Reinforcement Learning for Robotic ControlCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
qgym: A Gym for Training and Benchmarking RL-Based Quantum CompilationCode1
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
HAZARD Challenge: Embodied Decision Making in Dynamically Changing EnvironmentsCode1
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
Execution-based Code Generation using Deep Reinforcement LearningCode1
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage ProbabilityCode1
Behavior Proximal Policy OptimizationCode1
Hearts Gym: Learning Reinforcement Learning as a Team EventCode1
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Benchmark Results

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