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 9576–9600 of 15113 papers

TitleStatusHype
Behavior Priors for Efficient Reinforcement Learning—0
COG: Connecting New Skills to Past Experience with Offline Reinforcement LearningCode1
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement LearningCode1
Can Reinforcement Learning for Continuous Control Generalize Across Physics Engines?—0
Learning Financial Asset-Specific Trading Rules via Deep Reinforcement LearningCode1
Affordance as general value function: A computational model—0
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational SearchingCode0
Conservative Safety Critics for Exploration—0
Succinct and Robust Multi-Agent Communication With Temporal Message ControlCode1
Pairwise heuristic sequence alignment algorithm based on deep reinforcement learning—0
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning—0
Lyapunov-Based Reinforcement Learning State Estimator—0
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement LearningCode1
VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning—0
MELD: Meta-Reinforcement Learning from Images via Latent State ModelsCode1
Personalised Meta-path Generation for Heterogeneous GNNsCode1
Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision—0
Forethought and Hindsight in Credit Assignment—0
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards—0
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills—0
Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network—0
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control—0
How to Make Deep RL Work in PracticeCode0
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things—0
Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search—0
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Benchmark Results

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