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 9276–9300 of 15113 papers

TitleStatusHype
Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders—0
Reinforcement Learning of Cooperative Persuasive Dialogue Policies using Framing—0
Reinforcement Learning of Graph Neural Networks for Service Function Chaining—0
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions—0
Reinforcement Learning of Markov Decision Processes with Peak Constraints—0
Reinforcement learning of minimalist grammars—0
Reinforcement Learning of Minimalist Numeral Grammars—0
Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings—0
Reinforcement Learning of Multi-Issue Negotiation Dialogue Policies—0
Reinforcement learning of optimal active particle navigation—0
Reinforcement Learning of POMDPs using Spectral Methods—0
Reinforcement Learning of Question-Answering Dialogue Policies for Virtual Museum Guides—0
Reinforcement learning of rare diffusive dynamics—0
Reinforcement Learning of Sequential Price Mechanisms—0
Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection—0
Reinforcement Learning of Structured Control for Linear Systems with Unknown State Matrix—0
Reinforcement Learning of Theorem Proving—0
Reinforcement Learning of the Prediction Horizon in Model Predictive Control—0
Reinforcement Learning of Two-Issue Negotiation Dialogue Policies—0
Reinforcement Learning on Computational Resource Allocation of Cloud-based Wireless Networks—0
Reinforcement Learning on Encrypted Data—0
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning—0
Reinforcement Learning Policy Recommendation for Interbank Network Stability—0
Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information—0
Reinforcement Learning Portfolio Manager Framework with Monte Carlo Simulation—0
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Benchmark Results

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