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 5576–5600 of 15113 papers

TitleStatusHype
Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning—0
Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving—0
POMRL: No-Regret Learning-to-Plan with Increasing Horizons—0
Pontryagin Optimal Control via Neural NetworksCode0
Reinforcement Learning with Success Induced Task PrioritizationCode0
RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoTCode0
Offline Policy Optimization in RL with Variance Regularizaton—0
On Transforming Reinforcement Learning by Transformer: The Development Trajectory—0
On the Geometry of Reinforcement Learning in Continuous State and Action Spaces—0
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management—0
Backward Curriculum Reinforcement Learning—0
A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management—0
Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks—0
Don't do it: Safer Reinforcement Learning With Rule-based Guidance—0
Improving a sequence-to-sequence nlp model using a reinforcement learning policy algorithm—0
Representation Learning in Deep RL via Discrete Information Bottleneck—0
Towards automating Codenames spymasters with deep reinforcement learning—0
Offline Reinforcement Learning via Linear-Programming with Error-Bound Induced Constraints—0
Towards Learning Abstractions via Reinforcement Learning—0
On the Convergence of Discounted Policy Gradient Methods—0
Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach—0
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation—0
Strangeness-driven Exploration in Multi-Agent Reinforcement LearningCode0
Data-driven control of COVID-19 in buildings: a reinforcement-learning approach—0
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral SimilaritiesCode0
Show:102550
← PrevPage 224 of 605Next →

Benchmark Results

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