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

Showing 1000110050 of 15113 papers

TitleStatusHype
Intelligent Replication Management for HDFS Using Reinforcement Learning0
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged FraudstersCode1
A Survey of Knowledge-based Sequential Decision Making under Uncertainty0
Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary MissionsCode1
Adaptive trading strategies across liquidity pools0
A Framework for Studying Reinforcement Learning and Sim-to-Real in Robot Soccer0
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted RewardsCode0
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning0
Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement LearningCode0
Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning0
Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation0
Reinforcement Learning for Improving Object Detection0
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation0
A Survey on Reinforcement Learning for Combinatorial Optimization0
SuperSuit: Simple Microwrappers for Reinforcement Learning EnvironmentsCode1
Playing Catan with Cross-dimensional Neural Network0
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing0
Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs0
Forward and inverse reinforcement learning sharing network weights and hyperparameters0
Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles0
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization0
DRL-Based QoS-Aware Resource Allocation Scheme for Coexistence of Licensed and Unlicensed Users in LTE and Beyond0
Inverse Reinforcement Learning with Natural Language Goals0
An adaptive synchronization approach for weights of deep reinforcement learning0
Chrome Dino Run using Reinforcement Learning0
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving0
Cautious Adaptation For Reinforcement Learning in Safety-Critical SettingsCode1
Explainability in Deep Reinforcement Learning0
Safe Reinforcement Learning in Constrained Markov Decision ProcessesCode1
Reinforcement Learning with Quantum Variational CircuitsCode0
Model-Free Optimal Control of Linear Multi-Agent Systems via Decomposition and Hierarchical Approximation0
OR-Gym: A Reinforcement Learning Library for Operations Research ProblemsCode1
Sample-efficient Cross-Entropy Method for Real-time PlanningCode1
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning0
Adversary Agnostic Robust Deep Reinforcement Learning0
Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network0
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter0
Robust Image Matching By Dynamic Feature Selection0
Offline Meta-Reinforcement Learning with Advantage WeightingCode1
Reinforcement Learning with Trajectory Feedback0
A Review of Deep Reinforcement Learning for Smart Building Energy Management0
An ocular biomechanics environment for reinforcement learning0
Overcoming Model Bias for Robust Offline Deep Reinforcement Learning0
Model-Based Offline Planning0
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey0
An Intelligent Control Strategy for buck DC-DC Converter via Deep Reinforcement Learning0
Batch Value-function Approximation with Only RealizabilityCode0
Comparison of Model Predictive and Reinforcement Learning Methods for Fault Tolerant Control0
Fault-Tolerant Control of Degrading Systems with On-Policy Reinforcement Learning0
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Benchmark Results

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