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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2401–2425 of 15113 papers

TitleStatusHype
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning—0
A MultiModal Social Robot Toward Personalized Emotion Interaction—0
A Multimodal Learning-based Approach for Autonomous Landing of UAV—0
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning—0
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization—0
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies—0
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems—0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
BANANAS: Bayesian Optimization with Neural Networks for Neural Architecture Search—0
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization—0
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping—0
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning—0
A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications—0
Context-aware Active Multi-Step Reinforcement Learning—0
Balancing Two-Player Stochastic Games with Soft Q-Learning—0
Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols—0
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control—0
Balancing SoC in Battery Cells using Safe Action Perturbations—0
Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval—0
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization—0
Multiagent Model-based Credit Assignment for Continuous Control—0
Context-Aware Mobility Management in HetNets: A Reinforcement Learning Approach—0
Context-aware taxi dispatching at city-scale using deep reinforcement learning—0
Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving—0
Balancing Profit, Risk, and Sustainability for Portfolio Management—0
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Benchmark Results

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