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 27512775 of 15113 papers

TitleStatusHype
Infinite Time Horizon Safety of Bayesian Neural NetworksCode0
Influence-aware Memory Architectures for Deep Reinforcement LearningCode0
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement LearningCode0
Influence-Based Multi-Agent ExplorationCode0
Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learningCode0
Increasing the Action Gap: New Operators for Reinforcement LearningCode0
Incorporating Rivalry in Reinforcement Learning for a Competitive GameCode0
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in ClutterCode0
Increasing Data Efficiency of Driving Agent By World ModelCode0
Incentivizing Reasoning from Weak SupervisionCode0
Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement LearningCode0
Incentivizing Exploration In Reinforcement Learning With Deep Predictive ModelsCode0
Improving Unsupervised Hierarchical Representation with Reinforcement LearningCode0
A0C: Alpha Zero in Continuous Action SpaceCode0
Agent-State Construction with Auxiliary InputsCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
Gradient Importance Learning for Incomplete ObservationsCode0
Influencing Reinforcement Learning through Natural Language GuidanceCode0
Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language ModelsCode0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic NetworkCode0
Improving the Performance of Backward Chained Behavior Trees that use Reinforcement LearningCode0
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to ImitationCode0
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational SearchingCode0
Improving Generalization in Reinforcement Learning Training Regimes for Social Robot NavigationCode0
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Benchmark Results

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