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 1421–1430 of 15113 papers

TitleStatusHype
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement LearningCode1
Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory SystemsCode1
Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and ExploitationCode1
Mava: a research library for distributed multi-agent reinforcement learning in JAXCode1
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement LearningCode1
Distilling Reinforcement Learning Tricks for Video GamesCode1
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic RewardsCode1
Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-EnsembleCode1
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data AugmentationCode1
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros StudyCode1
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Benchmark Results

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