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 3801–3825 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs—0
Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA—0
A Visual Communication Map for Multi-Agent Deep Reinforcement Learning—0
A Model-Based Reinforcement Learning Approach for PID Design—0
Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments—0
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images—0
Deep reinforcement learning under signal temporal logic constraints using Lagrangian relaxation—0
Deep Reinforcement Learning using Capsules in Advanced Game Environments—0
Deep Reinforcement Learning using Cyclical Learning Rates—0
Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey—0
Deep Reinforcement Learning with Decorrelation—0
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization—0
Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning—0
Deep Reinforcement Learning With Adaptive Combined Critics—0
Deep Reinforcement Learning with Adjustments—0
Avoiding Wireheading with Value Reinforcement Learning—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning—0
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Deep Reinforcement Learning with Explicit Context Representation—0
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Assume-Guarantee Reinforcement Learning—0
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Benchmark Results

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