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 9301–9325 of 15113 papers

TitleStatusHype
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction—0
Metalearning Using Structure-rich Pipeline Representations for Better AutoML—0
RL-Controller: a reinforcement learning framework for active structural control—0
Hybrid computer approach to train a machine learning system—0
A Survey of Forex and Stock Price Prediction Using Deep Learning—0
Constrained Text Generation with Global Guidance -- Case Study on CommonGen—0
Analyzing the Hidden Activations of Deep Policy Networks: Why Representation Matters—0
Adapting User Interfaces with Model-based Reinforcement Learning—0
A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance—0
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
A Quadratic Actor Network for Model-Free Reinforcement LearningCode0
Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options—0
A Reinforcement Learning Based Approach to Play Calling in Football—0
Multi-Task Federated Reinforcement Learning with Adversaries—0
Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks—0
Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning—0
Symbolic Reinforcement Learning for Safe RAN Control—0
Policy Search with Rare Significant Events: Choosing the Right Partner to Cooperate withCode0
Streaming Linear System Identification with Reverse Experience Replay—0
Using Cognitive Models to Train Warm Start Reinforcement Learning Agents for Human-Computer Interactions—0
Maximum Entropy RL (Provably) Solves Some Robust RL Problems—0
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning—0
Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem—0
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning—0
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems—0
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Benchmark Results

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