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 8551–8575 of 15113 papers

TitleStatusHype
Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions—0
Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution SystemsCode1
Metalearning Using Structure-rich Pipeline Representations for Better AutoML—0
A Survey of Forex and Stock Price Prediction Using Deep Learning—0
Hybrid computer approach to train a machine learning system—0
Solving Compositional Reinforcement Learning Problems via Task ReductionCode1
RL-Controller: a reinforcement learning framework for active structural control—0
Constrained Text Generation with Global Guidance -- Case Study on CommonGen—0
Large Batch Simulation for Deep Reinforcement LearningCode1
Analyzing the Hidden Activations of Deep Policy Networks: Why Representation Matters—0
Generalizable Episodic Memory for Deep Reinforcement LearningCode1
A Reinforcement Learning Based Approach to Play Calling in Football—0
A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance—0
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
Multi-Task Federated Reinforcement Learning with Adversaries—0
Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning—0
XDO: A Double Oracle Algorithm for Extensive-Form GamesCode1
Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks—0
Symbolic Reinforcement Learning for Safe RAN Control—0
Policy Search with Rare Significant Events: Choosing the Right Partner to Cooperate withCode0
Adapting User Interfaces with Model-based Reinforcement Learning—0
Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options—0
A Quadratic Actor Network for Model-Free Reinforcement LearningCode0
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning—0
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning—0
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Benchmark Results

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