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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5301–5325 of 15113 papers

TitleStatusHype
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers—0
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic—0
Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions—0
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning—0
Improving Hyperparameter Optimization by Planning Ahead—0
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning—0
Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learning—0
Improving Interactive Reinforcement Agent Planning with Human Demonstration—0
Improving interactive reinforcement learning: What makes a good teacher?—0
Improving Intrinsic Exploration with Language Abstractions—0
Improving Learning from Demonstrations by Learning from Experience—0
Improving Learning to Branch via Reinforcement Learning—0
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning—0
Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation—0
Improving Mixed-Criticality Scheduling with Reinforcement Learning—0
Improving Model and Search for Computer Go—0
Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning—0
Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback—0
Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization—0
Improving Neural Machine Translation for Sanskrit-English—0
Improving Neural Relation Extraction with Positive and Unlabeled Learning—0
Improving Offline Reinforcement Learning with Inaccurate Simulators—0
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks—0
Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents—0
Improving Policy Gradient by Exploring Under-appreciated Rewards—0
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Benchmark Results

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