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 9251–9275 of 15113 papers

TitleStatusHype
Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents—0
AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning—0
Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks—0
Self-Imitation Learning by Planning—0
Nearly Horizon-Free Offline Reinforcement Learning—0
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning—0
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication—0
Discriminator Augmented Model-Based Reinforcement Learning—0
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target PredictionCode0
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation—0
CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL—0
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap—0
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework—0
Hamiltonian Policy Optimization in Reinforcement Learning—0
Learning 6DoF Grasping Using Reward-Consistent Demonstration—0
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks—0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
Replacing Rewards with Examples: Example-Based Policy Search via Recursive ClassificationCode0
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement LearningCode0
Reinforcement Learning based on MPC/MHE for Unmodeled and Partially Observable Dynamics—0
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks—0
Online Baum-Welch algorithm for Hierarchical Imitation LearningCode0
Variational quantum compiling with double Q-learning—0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Reinforcement Learning based on Scenario-tree MPC for ASVs—0
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Benchmark Results

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