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 2151–2200 of 15113 papers

TitleStatusHype
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
SNAS: Stochastic Neural Architecture SearchCode1
Soft Actor-Critic Algorithms and ApplicationsCode1
Off-Policy Deep Reinforcement Learning without ExplorationCode1
Quantifying Generalization in Reinforcement LearningCode1
An Introduction to Deep Reinforcement LearningCode1
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Exploration by Random Network DistillationCode1
Gated Hierarchical Attention for Image CaptioningCode1
Model-Based Active ExplorationCode1
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich TasksCode1
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement LearningCode1
Optimization of Molecules via Deep Reinforcement LearningCode1
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action SpaceCode1
Fast Context Adaptation via Meta-LearningCode1
Actor-Attention-Critic for Multi-Agent Reinforcement LearningCode1
Solving Statistical Mechanics Using Variational Autoregressive NetworksCode1
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement LearningCode1
Generalizing Across Multi-Objective Reward Functions in Deep Reinforcement LearningCode1
Negative Update Intervals in Deep Multi-Agent Reinforcement LearningCode1
SAI, a Sensible Artificial Intelligence that plays GoCode1
Multi-Hop Knowledge Graph Reasoning with Reward ShapingCode1
Decoupling Strategy and Generation in Negotiation DialoguesCode1
Adversarial Deep Reinforcement Learning in Portfolio ManagementCode1
Reinforcement Learning for Relation Classification from Noisy DataCode1
Multi-Agent Generative Adversarial Imitation LearningCode1
Is Q-learning Provably Efficient?Code1
DARTS: Differentiable Architecture SearchCode1
Maximum a Posteriori Policy OptimisationCode1
Reward Constrained Policy OptimizationCode1
Deep Reinforcement Learning For Sequence to Sequence ModelsCode1
Verifiable Reinforcement Learning via Policy ExtractionCode1
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and ReviewCode1
Toward Diverse Text Generation with Inverse Reinforcement LearningCode1
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character SkillsCode1
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement LearningCode1
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement LearningCode1
World ModelsCode1
Simple random search provides a competitive approach to reinforcement learningCode1
Addressing Function Approximation Error in Actor-Critic MethodsCode1
Reinforcement Learning on Web Interfaces Using Workflow-Guided ExplorationCode1
Fully Decentralized Multi-Agent Reinforcement Learning with Networked AgentsCode1
Meta-Reinforcement Learning of Structured Exploration StrategiesCode1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Mean Field Multi-Agent Reinforcement LearningCode1
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner ArchitecturesCode1
Safe Exploration in Continuous Action SpacesCode1
Logically-Constrained Reinforcement LearningCode1
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Benchmark Results

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