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 7251–7275 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning for Legged Locomotion—0
Target Network and Truncation Overcome The Deadly Triad in Q-Learning—0
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions—0
GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning—0
Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning—0
Intrinsically-Motivated Reinforcement Learning: A Brief Introduction—0
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model—0
Deep Q-network using reservoir computing with multi-layered readout—0
Reasoning about Counterfactuals to Improve Human Inverse Reinforcement LearningCode0
Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning—0
On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical EfficiencyCode0
The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches—0
Quantum Reinforcement Learning via Policy Iteration—0
Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSP—0
Reliable validation of Reinforcement Learning Benchmarks—0
Evolving Curricula with Regret-Based Environment Design—0
Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman ProblemCode0
A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open ProblemsCode0
Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning—0
Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power SystemsCode0
Learning in Sparse Rewards settings through Quality-Diversity algorithms—0
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from ImagesCode0
DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction—0
Hierarchical Reinforcement Learning with AI Planning ModelsCode0
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes—0
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Benchmark Results

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