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 9651–9675 of 15113 papers

TitleStatusHype
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function ApproximatorCode1
Chance-Constrained Control with Lexicographic Deep Reinforcement Learning—0
D2RL: Deep Dense Architectures in Reinforcement LearningCode1
A Reinforcement Learning Approach to Health Aware Control Strategy—0
Knowledge-guided Open Attribute Value Extraction with Reinforcement LearningCode1
Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification—0
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous ControlCode1
Average-reward model-free reinforcement learning: a systematic review and literature mapping—0
DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPsCode0
Model-Based Inverse Reinforcement Learning from Visual Demonstrations—0
Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading—0
Neural Algorithms for Graph Navigation—0
Scalable Evolution Strategies Pipeline for Solving the Vehicle Routing Problem—0
Learning Lower Bounds for Graph Exploration With Reinforcement Learning—0
Learning Elimination Ordering for Tree Decomposition Problem—0
Assessment of Reward Functions in Reinforcement Learning for Multi-Modal Urban Traffic Control under Real-World limitations—0
Approximate information state for approximate planning and reinforcement learning in partially observed systemsCode1
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning—0
Robot Navigation in Constrained Pedestrian Environments using Reinforcement LearningCode1
Reinforcement Learning for Efficient and Tuning-Free Link Adaptation—0
Uncertainty-aware Contact-safe Model-based Reinforcement Learning—0
Decomposability and Parallel Computation of Multi-Agent LQR—0
DOOM: A Novel Adversarial-DRL-Based Op-Code Level Metamorphic Malware Obfuscator for the Enhancement of IDS—0
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling—0
Few-shot model-based adaptation in noisy conditions—0
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Benchmark Results

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