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 12951–13000 of 15113 papers

TitleStatusHype
Predicting Research Trends From ArxivCode0
Concurrent Meta Reinforcement LearningCode0
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning AlgorithmsCode0
Continual Learning Using World Models for Pseudo-Rehearsal—0
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation—0
Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning—0
Minigo: A Case Study in Reproducing Reinforcement Learning Research—0
Training in Task Space to Speed Up and Guide Reinforcement Learning—0
simple_rl: Reproducible Reinforcement Learning in PythonCode0
Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning—0
Online Data Poisoning Attack—0
Using Natural Language for Reward Shaping in Reinforcement LearningCode0
Viewpoint Optimization for Autonomous Strawberry Harvesting with Deep Reinforcement LearningCode0
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future—0
Hybrid Actor-Critic Reinforcement Learning in Parameterized Action SpaceCode0
Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning—0
NoRML: No-Reward Meta Learning—0
Budgeted Reinforcement Learning in Continuous State SpaceCode0
Asynchronous Episodic Deep Deterministic Policy Gradient: Towards Continuous Control in Computationally Complex EnvironmentsCode0
Hacking Google reCAPTCHA v3 using Reinforcement Learning—0
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning—0
Discovering Options for Exploration by Minimizing Cover Time—0
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer LearningCode0
Automating Predictive Modeling Process using Reinforcement Learning—0
OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras—0
Straight to the point: reinforcement learning for user guidance in ultrasound—0
Model-Based Reinforcement Learning for AtariCode0
TrojDRL: Trojan Attacks on Deep Reinforcement Learning AgentsCode0
Learning To Follow Directions in Street ViewCode0
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation—0
Unsupervised Attention Mechanism across Neural Network LayersCode0
Neural Packet Classification—0
Unifying Ensemble Methods for Q-learning via Social Choice Theory—0
Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks—0
Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks—0
Introspection Learning—0
Diagnosing Bottlenecks in Deep Q-learning AlgorithmsCode0
Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?—0
Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering—0
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies—0
Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings—0
S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay—0
Long-Range Indoor Navigation with PRM-RL—0
Learning Extreme Hummingbird Maneuvers on Flapping Wing Robots—0
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence—0
Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and AnimalsCode0
Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning—0
Distributionally Robust Reinforcement Learning—0
A General Framework for Structured Learning of Mechanical SystemsCode0
Generative Memory for Lifelong Reinforcement Learning—0
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Benchmark Results

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