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 1321–1330 of 15113 papers

TitleStatusHype
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
A Reinforcement Learning Approach for Rebalancing Electric Vehicle Sharing SystemsCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
Contextualized Rewriting for Text SummarizationCode1
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence GenerationCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
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Benchmark Results

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