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 10001–10025 of 15113 papers

TitleStatusHype
Multi-condition multi-objective optimization using deep reinforcement learning—0
Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach—0
Multi-echelon Supply Chains with Uncertain Seasonal Demands and Lead Times Using Deep Reinforcement Learning—0
Multi-Fidelity Policy Gradient Algorithms—0
Multi-fidelity reinforcement learning framework for shape optimization—0
Multifidelity Reinforcement Learning with Control Variates—0
Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach—0
Multi-focus Attention Network for Efficient Deep Reinforcement Learning—0
Multi-Issue Bargaining With Deep Reinforcement Learning—0
Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning—0
Multi-level Explanation of Deep Reinforcement Learning-based Scheduling—0
Multi-Level Policy and Reward Reinforcement Learning for Image Captioning—0
Multi-market Energy Optimization with Renewables via Reinforcement Learning—0
Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach—0
Multimodal Deep Reinforcement Learning for Portfolio Optimization—0
Multimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning—0
Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning—0
Multimodal Hierarchical Reinforcement Learning Policy for Task-Oriented Visual Dialog—0
Multimodal Machine Translation with Reinforcement Learning—0
Multi-modal reward for visual relationships-based image captioning—0
Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learning—0
Multi-Modal Transformer and Reinforcement Learning-based Beam Management—0
Multi-Objective Autonomous Braking System using Naturalistic Dataset—0
Multi-Objective Decision Transformers for Offline Reinforcement Learning—0
Evolving Pareto-Optimal Actor-Critic Algorithms for Generalizability and Stability—0
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Benchmark Results

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