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 5476–5500 of 15113 papers

TitleStatusHype
Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning—0
Robust Bayesian optimization with reinforcement learned acquisition functions—0
Robust Constrained Reinforcement Learning—0
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification—0
Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach—0
Exploring the Noise Resilience of Successor Features and Predecessor Features Algorithms in One and Two-Dimensional Environments—0
Robust Decision Transformer: Tackling Data Corruption in Offline RL via Sequence Modeling—0
Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks—0
Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems—0
Robust Deep Reinforcement Learning for Extractive Legal Summarization—0
Robust Deep Reinforcement Learning with Adversarial Attacks—0
Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents—0
Robust Domain Randomised Reinforcement Learning through Peer-to-Peer Distillation—0
Robust Domain Randomization for Reinforcement Learning—0
Robust Dual View Deep Agent—0
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach—0
Robust Entropy-regularized Markov Decision Processes—0
Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning—0
Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning—0
Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks—0
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training—0
Robustifying Reinforcement Learning Policies with L_1 Adaptive Control—0
Robust Image Matching By Dynamic Feature Selection—0
Robust Imitation via Decision-Time Planning—0
Robust Imitation via Mirror Descent Inverse Reinforcement Learning—0
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Benchmark Results

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