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 8501–8525 of 15113 papers

TitleStatusHype
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication—0
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation—0
CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL—0
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target PredictionCode0
Discriminator Augmented Model-Based Reinforcement Learning—0
Hamiltonian Policy Optimization in Reinforcement Learning—0
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap—0
Learning 6DoF Grasping Using Reward-Consistent Demonstration—0
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework—0
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks—0
Replacing Rewards with Examples: Example-Based Policy Search via Recursive ClassificationCode0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement LearningCode0
Online Baum-Welch algorithm for Hierarchical Imitation LearningCode0
Variational quantum compiling with double Q-learning—0
Reinforcement Learning based on Scenario-tree MPC for ASVs—0
Provably Correct Optimization and Exploration with Non-linear PoliciesCode0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Reinforcement Learning based on MPC/MHE for Unmodeled and Partially Observable Dynamics—0
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks—0
Improving Actor-Critic Reinforcement Learning via Hamiltonian Monte Carlo Method—0
IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning—0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Bayesian Distributional Policy Gradients—0
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning—0
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Benchmark Results

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