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 10801–10825 of 15113 papers

TitleStatusHype
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
A non-cooperative meta-modeling game for automated third-party calibrating, validating, and falsifying constitutive laws with parallelized adversarial attacks—0
K-spin Hamiltonian for quantum-resolvable Markov decision processes—0
Aspect and Opinion Aware Abstractive Review Summarization with Reinforced Hard Typed Decoder—0
A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding—0
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control—0
Kernel-Based Reinforcement Learning: A Finite-Time AnalysisCode0
Reinforcement Learning via Reasoning from Demonstration—0
PatchAttack: A Black-box Texture-based Attack with Reinforcement LearningCode1
Deep Reinforcement Learning for Process Control: A Primer for Beginners—0
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning—0
Self Punishment and Reward Backfill for Deep Q-LearningCode0
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels—0
Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion—0
Topological Quantum Compiling with Reinforcement LearningCode1
Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control—0
Policy Gradient using Weak Derivatives for Reinforcement Learning—0
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization—0
Stochastic Approximation with Markov Noise: Analysis and applications in reinforcement learning—0
Adaptive Stress Testing without Domain Heuristics using Go-Explore—0
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card GameCode0
CURL: Contrastive Unsupervised Representations for Reinforcement LearningCode1
Adaptive Transformers in RLCode1
Continual Learning with Gated Incremental Memories for sequential data processingCode1
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Benchmark Results

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