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 6076–6100 of 15113 papers

TitleStatusHype
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
Low-Thrust Orbital Transfer using Dynamics-Agnostic Reinforcement Learning—0
Learning Algorithms for Intelligent Agents and Mechanisms—0
Distributionally Adaptive Meta Reinforcement Learning—0
Deep Inventory Management—0
Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning—0
A Novel Entropy-Maximizing TD3-based Reinforcement Learning for Automatic PID Tuning—0
Query The Agent: Improving sample efficiency through epistemic uncertainty estimation—0
Neural Distillation as a State Representation Bottleneck in Reinforcement Learning—0
On Neural Consolidation for Transfer in Reinforcement Learning—0
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement LearningCode0
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning—0
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement LearningCode0
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees—0
Using Deep Reinforcement Learning for mmWave Real-Time Scheduling—0
Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control—0
Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control—0
Hyperbolic Deep Reinforcement Learning—0
Learning Dynamic Abstract Representations for Sample-Efficient Reinforcement Learning—0
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors—0
Learning Perception-Aware Agile Flight in Cluttered Environments—0
Accelerate Reinforcement Learning with PID Controllers in the Pendulum SimulationsCode0
Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders—0
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning—0
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient—0
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Benchmark Results

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