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 8651–8700 of 15113 papers

TitleStatusHype
Foresee then Evaluate: Decomposing Value Estimation with Latent Future PredictionCode0
Adversarial Environment Generation for Learning to Navigate the WebCode0
Minimax Model Learning—0
Offline Reinforcement Learning with Pseudometric Learning—0
Model-based Constrained Reinforcement Learning using Generalized Control Barrier FunctionCode1
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent GamesCode1
Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB trafficCode1
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational GraphCode1
Reinforcement Learning for Adaptive Mesh Refinement—0
Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach—0
Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning—0
Hamiltonian Policy Optimization—0
Learning for Visual Navigation by Imagining the Success—0
Exploration and Incentives in Reinforcement Learning—0
Where the Action is: Let's make Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problems work!—0
Reducing Conservativeness Oriented Offline Reinforcement Learning—0
Optimal control of point-to-point navigation in turbulent time-dependent flows using Reinforcement Learning—0
Revisiting Peng's Q(λ) for Modern Reinforcement Learning—0
Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction ReportCode1
Safe Distributional Reinforcement Learning—0
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision—0
Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online PlanningCode1
On the Importance of Hyperparameter Optimization for Model-based Reinforcement LearningCode1
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information BottleneckCode0
Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach—0
Reinforcement learning approach for resource allocation in humanitarian logistics—0
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions—0
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement LearningCode1
Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control—0
Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning—0
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods—0
Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning—0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning—0
On The Effect of Auxiliary Tasks on Representation Dynamics—0
Modular Object-Oriented Games: A Task Framework for Reinforcement Learning, Psychology, and NeuroscienceCode1
Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians—0
No-Regret Reinforcement Learning with Heavy-Tailed Rewards—0
Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning—0
Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism—0
Information Directed Reward Learning for Reinforcement LearningCode1
Fast Approximate Solutions using Reinforcement Learning for Dynamic Capacitated Vehicle Routing with Time Windows—0
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning—0
Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning—0
Combining Off and On-Policy Training in Model-Based Reinforcement Learning—0
Towards Safe Continuing Task Reinforcement Learning—0
Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal LogicCode0
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference LearningCode0
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation—0
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Benchmark Results

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