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 9801–9825 of 15113 papers

TitleStatusHype
Entropy Regularization for Mean Field Games with Learning—0
Graph-based Heuristic Search for Module Selection Procedure in Neural Module Network—0
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement LearningCode1
PettingZoo: Gym for Multi-Agent Reinforcement LearningCode2
Toolpath design for additive manufacturing using deep reinforcement learning—0
Teacher-Critical Training Strategies for Image Captioning—0
Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks—0
Reannealing of Decaying Exploration Based On Heuristic Measure in Deep Q-Network—0
Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive LearningCode0
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current PolicyCode0
Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments—0
Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration—0
Cross Learning in Deep Q-Networks—0
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
Jointly-Trained State-Action Embedding for Efficient Reinforcement Learning—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator—0
The Emergence of Individuality in Multi-Agent Reinforcement Learning—0
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning—0
Transfer among Agents: An Efficient Multiagent Transfer Learning Framework—0
Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs—0
MDP Playground: Controlling Orthogonal Dimensions of Hardness in Toy Environments—0
Policy Gradient with Expected Quadratic Utility Maximization: A New Mean-Variance Approach in Reinforcement Learning—0
Neuron Activation Analysis for Multi-Joint Robot Reinforcement Learning—0
Deep Reinforcement Learning for DER Cyber-Attack Mitigation—0
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Benchmark Results

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