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 1451–1475 of 15113 papers

TitleStatusHype
Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image TranslationCode1
Revisiting the Weaknesses of Reinforcement Learning for Neural Machine TranslationCode1
Deep Reinforcement Learning for Conservation DecisionsCode1
Randomized Exploration for Reinforcement Learning with General Value Function ApproximationCode1
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot SoccerCode1
Efficient (Soft) Q-Learning for Text Generation with Limited Good DataCode1
Learning Intrusion Prevention Policies through Optimal StoppingCode1
Deep Reinforcement Learning based Group Recommender SystemCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Reinforcement Learning as One Big Sequence Modeling ProblemCode1
A Minimalist Approach to Offline Reinforcement LearningCode1
Recomposing the Reinforcement Learning Building Blocks with HypernetworksCode1
A Game-Theoretic Approach to Multi-Agent Trust Region OptimizationCode1
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor RepresentationCode1
WAX-ML: A Python library for machine learning and feedback loops on streaming dataCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Pretrained Encoders are All You NeedCode1
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingCode1
Pretraining Representations for Data-Efficient Reinforcement LearningCode1
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RLCode1
Learning Markov State Abstractions for Deep Reinforcement LearningCode1
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement LearningCode1
Dynamic Sparse Training for Deep Reinforcement LearningCode1
Causal Influence Detection for Improving Efficiency in Reinforcement LearningCode1
Task-driven Semantic Coding via Reinforcement LearningCode1
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Benchmark Results

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