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 671–680 of 15113 papers

TitleStatusHype
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable EnvironmentCode1
Continuous control with deep reinforcement learningCode1
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement LearningCode1
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Benchmark Results

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