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 7301–7325 of 15113 papers

TitleStatusHype
Learning Relative Return Policies With Upside-Down Reinforcement Learning—0
Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors—0
Comparative analysis of machine learning methods for active flow control—0
Consistent Dropout for Policy Gradient Reinforcement Learning—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four—0
Reinforcement Learning from Demonstrations by Novel Interactive Expert and Application to Automatic Berthing Control Systems for Unmanned Surface Vessel—0
Multi-fidelity reinforcement learning framework for shape optimization—0
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning—0
Reward-Free Policy Space Compression for Reinforcement Learning—0
A policy gradient approach for optimization of smooth risk measures—0
Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach—0
Continual Auxiliary Task Learning—0
A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning—0
Behaviour-neutral Smart Charging of Plugin Electric Vehicles: Reinforcement learning approach—0
Autonomous Warehouse Robot using Deep Q-Learning—0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories—0
Accelerating Primal-dual Methods for Regularized Markov Decision Processes—0
Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks—0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Rule Mining over Knowledge Graphs via Reinforcement Learning—0
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing—0
PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning—0
Selective Credit Assignment—0
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Benchmark Results

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