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 10651–10675 of 15113 papers

TitleStatusHype
Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing—0
Representations for Stable Off-Policy Reinforcement Learning—0
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning—0
MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System—0
Vizarel: A System to Help Better Understand RL Agents—0
Weakness Analysis of Cyberspace Configuration Based on Reinforcement Learning—0
On the Reliability and Generalizability of Brain-inspired Reinforcement Learning AlgorithmsCode0
Learning Retrospective Knowledge with Reverse Reinforcement Learning—0
Fast reinforcement learning with generalized policy updates—0
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces—0
Attention or memory? Neurointerpretable agents in space and time—0
Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization—0
Learning to Prune Deep Neural Networks via Reinforcement Learning—0
EVO-RL: Evolutionary-Driven Reinforcement Learning—0
A Natural Actor-Critic Algorithm with Downside Risk Constraints—0
Discourse Coherence, Reference Grounding and Goal Oriented Dialogue—0
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads—0
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods—0
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning—0
Towards a practical measure of interference for reinforcement learning—0
Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach—0
Sharp Analysis of Smoothed Bellman Error Embedding—0
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement LearningCode0
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems—0
A deep reinforcement learning model based on deterministic policy gradient for collective neural crest cell migration—0
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Benchmark Results

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