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 11151–11175 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?—0
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning—0
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication—0
Learning Sparse Rewarded Tasks from Sub-Optimal DemonstrationsCode0
Constrained-Space Optimization and Reinforcement Learning for Complex Tasks—0
Controlling Rayleigh-Bénard convection via Reinforcement Learning—0
Learning to Ask Medical Questions using Reinforcement LearningCode0
Leverage the Average: an Analysis of KL Regularization in RL—0
Augmented Q Imitation Learning (AQIL)Code0
Exploration in Action SpaceCode0
Straight to the Point: Fast-forwarding Videos via Reinforcement Learning Using Textual DataCode0
Mimicking Evolution with Reinforcement Learning—0
Robotic Table Tennis with Model-Free Reinforcement Learning—0
Optimal Bidding Strategy without Exploration in Real-time Bidding—0
Optimising Lockdown Policies for Epidemic Control using Reinforcement LearningCode0
Suphx: Mastering Mahjong with Deep Reinforcement LearningCode0
Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties—0
Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning—0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward ShapingCode0
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement LearningCode0
Learning medical triage from clinicians using Deep Q-Learning—0
AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference—0
Adaptive Reward-Poisoning Attacks against Reinforcement Learning—0
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms—0
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Benchmark Results

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