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 12351–12400 of 15113 papers

TitleStatusHype
Injecting Prior Knowledge for Transfer Learning into Reinforcement Learning Algorithms using Logic Tensor Networks—0
Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Epistemic Risk-Sensitive Reinforcement Learning—0
Cross-View Policy Learning for Street NavigationCode0
Sub-policy Adaptation for Hierarchical Reinforcement Learning—0
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning—0
Deep Reinforcement Learning for Cyber Security—0
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural RewardsCode0
Conditioning of Reinforcement Learning Agents and its Policy Regularization Application—0
Goal-conditioned Imitation LearningCode0
Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks—0
Reinforcement Knowledge Graph Reasoning for Explainable RecommendationCode0
When to use parametric models in reinforcement learning?Code1
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function—0
Adaptive Optimal Control for Reference Tracking Independent of Exo-System Dynamics—0
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning—0
Sub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization—0
Reinforcement Learning of Minimalist Numeral Grammars—0
Reinforcement Learning for Channel Coding: Learned Bit-Flipping DecodingCode0
Reinforcement Learning for Integer Programming: Learning to Cut—0
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics—0
Learning to Score Behaviors for Guided Policy OptimizationCode0
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer—0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression—0
Causal Discovery with Reinforcement Learning—0
Exploration via Hindsight Goal GenerationCode0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
A Survey of Reinforcement Learning Informed by Natural Language—0
Neural Keyphrase Generation via Reinforcement Learning with Adaptive RewardsCode0
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal—0
Gossip-based Actor-Learner Architectures for Deep Reinforcement LearningCode0
Curiosity-Driven Multi-Criteria Hindsight Experience ReplayCode0
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement LearningCode0
Neural Heterogeneous Scheduler—0
Transfer Learning by Modeling a Distribution over Policies—0
SVRG for Policy Evaluation with Fewer Gradient EvaluationsCode0
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling—0
Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach—0
Preference-based Interactive Multi-Document SummarisationCode0
Worst-Case Regret Bounds for Exploration via Randomized Value Functions—0
Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism—0
Ego-Pose Estimation and Forecasting as Real-Time PD ControlCode0
Improving Exploration in Soft-Actor-Critic with Normalizing Flows PoliciesCode0
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP—0
Towards Interpretable Reinforcement Learning Using Attention Augmented AgentsCode0
Clustered Reinforcement Learning—0
An Extensible Interactive Interface for Agent Design—0
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Benchmark Results

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