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

Showing 851860 of 15113 papers

TitleStatusHype
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Extreme Q-Learning: MaxEnt RL without EntropyCode1
Emergent collective intelligence from massive-agent cooperation and competitionCode1
Learning to Maximize Mutual Information for Dynamic Feature SelectionCode1
Environment Agnostic Representation for Visual Reinforcement LearningCode1
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous NavigationCode1
Self-Activating Neural Ensembles for Continual Reinforcement LearningCode1
Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression SearchCode1
Transformer in Transformer as Backbone for Deep Reinforcement LearningCode1
Risk-Sensitive Policy with Distributional Reinforcement LearningCode1
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Benchmark Results

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