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 10201–10225 of 15113 papers

TitleStatusHype
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification—0
Negotiating Team Formation Using Deep Reinforcement Learning—0
Quality of service based radar resource management using deep reinforcement learning—0
Learning by Competition of Self-Interested Reinforcement Learning AgentsCode0
Imitation with Neural Density Models—0
Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification—0
A case for new neural networks smoothness constraints—0
Chance-Constrained Control with Lexicographic Deep Reinforcement Learning—0
A Reinforcement Learning Approach to Health Aware Control Strategy—0
Average-reward model-free reinforcement learning: a systematic review and literature mapping—0
DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPsCode0
Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading—0
Model-Based Inverse Reinforcement Learning from Visual Demonstrations—0
Neural Algorithms for Graph Navigation—0
Scalable Evolution Strategies Pipeline for Solving the Vehicle Routing Problem—0
Variational Dynamic for Self-Supervised Exploration in Deep 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
Learning Lower Bounds for Graph Exploration With Reinforcement Learning—0
Interpretable Disease Prediction based on Reinforcement Path Reasoning over Knowledge Graphs—0
DOOM: A Novel Adversarial-DRL-Based Op-Code Level Metamorphic Malware Obfuscator for the Enhancement of IDS—0
Few-shot model-based adaptation in noisy conditions—0
Decomposability and Parallel Computation of Multi-Agent LQR—0
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling—0
Hyperparameter Auto-tuning in Self-Supervised Robotic LearningCode0
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Benchmark Results

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