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 10326–10350 of 15113 papers

TitleStatusHype
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
Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments—0
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current PolicyCode0
Cross Learning in Deep Q-Networks—0
Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Agent Environment Cycle Games—0
Jointly-Trained State-Action Embedding for Efficient Reinforcement Learning—0
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon—0
Deep Reinforcement Learning for DER Cyber-Attack Mitigation—0
Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs—0
The Emergence of Individuality in Multi-Agent Reinforcement Learning—0
MDP Playground: Controlling Orthogonal Dimensions of Hardness in Toy Environments—0
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning—0
Neuron Activation Analysis for Multi-Joint Robot Reinforcement Learning—0
Policy Gradient with Expected Quadratic Utility Maximization: A New Mean-Variance Approach in Reinforcement Learning—0
Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks—0
What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator—0
Transfer among Agents: An Efficient Multiagent Transfer Learning Framework—0
Virtual Experience to Real World Application: Sidewalk Obstacle Avoidance Using Reinforcement Learning for Visually Impaired—0
Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning—0
Machine Learning in Event-Triggered Control: Recent Advances and Open Issues—0
Scalable Deep Reinforcement Learning for Ride-Hailing—0
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Benchmark Results

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