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 4176–4200 of 15113 papers

TitleStatusHype
Relate to Predict: Towards Task-Independent Knowledge Representations for Reinforcement Learning—0
Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model CheckingCode1
Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning—0
Effects of Spectral Normalization in Multi-agent Reinforcement LearningCode0
Reinforcement Learning for Predicting Traffic Accidents—0
Reinforcement Learning and Mixed-Integer Programming for Power Plant Scheduling in Low Carbon Systems: Comparison and Hybridisation—0
Near-Optimal Differentially Private Reinforcement Learning—0
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games—0
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
Confidence-Conditioned Value Functions for Offline Reinforcement Learning—0
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces—0
Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk—0
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning—0
Reinforcement Learning for Resilient Power Grids—0
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement—0
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble—0
Adaptive Risk-Aware Bidding with Budget Constraint in Display AdvertisingCode0
First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation—0
Few-Shot Preference Learning for Human-in-the-Loop RL—0
Understanding Self-Predictive Learning for Reinforcement Learning—0
Reinforcement Learning for UAV control with Policy and Reward Shaping—0
Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle ApproachCode0
Safe Inverse Reinforcement Learning via Control Barrier Function—0
Scalable Planning and Learning Framework Development for Swarm-to-Swarm Engagement Problems—0
Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement LearningCode0
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Benchmark Results

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