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 8301–8350 of 15113 papers

TitleStatusHype
Curious Exploration and Return-based Memory Restoration for Deep Reinforcement LearningCode0
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning—0
Better than the Best: Gradient-based Improper Reinforcement Learning for Network Scheduling—0
Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing—0
Mitigating Political Bias in Language Models Through Reinforced Calibration—0
Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning—0
Discrete-Time Mean Field Control with Environment States—0
Emotional Contagion-Aware Deep Reinforcement Learning for Antagonistic Crowd Simulation—0
Adapting to Reward Progressivity via Spectral Reinforcement LearningCode0
Hypernetwork Dismantling via Deep Reinforcement Learning—0
Constructions in combinatorics via neural networksCode1
Medium Access using Distributed Reinforcement Learning for IoTs with Low-Complexity Wireless Transceivers—0
Pre-training of Deep RL Agents for Improved Learning under Domain Randomization—0
What is Going on Inside Recurrent Meta Reinforcement Learning Agents?—0
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response—0
Adversarial Inverse Reinforcement Learning for Mean Field Games—0
Reward (Mis)design for Autonomous Driving—0
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning—0
A Reinforcement Learning Environment for Polyhedral Optimizations—0
End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning—0
Implementing Reinforcement Learning Algorithms in Retail Supply Chains with OpenAI Gym Toolkit—0
Controlling earthquake-like instabilities using artificial intelligence—0
A Scalable and Reproducible System-on-Chip Simulation for Reinforcement LearningCode1
Adaptive Adversarial Training for Meta Reinforcement Learning—0
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients—0
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learningCode0
ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming—0
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
A Deep Reinforcement Learning Approach for the Meal Delivery Problem—0
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand SystemsCode1
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies—0
Safe Chance Constrained Reinforcement Learning for Batch Process ControlCode0
Reinforcement Learning using Guided Observability—0
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention—0
A learning gap between neuroscience and reinforcement learningCode0
Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data—0
Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control ProblemCode1
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents—0
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks—0
Tackling Variabilities in Autonomous Driving—0
Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems—0
CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles—0
DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future—0
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning—0
Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning—0
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning—0
MBRL-Lib: A Modular Library for Model-based Reinforcement LearningCode2
Outcome-Driven Reinforcement Learning via Variational Inference—0
Model-predictive control and reinforcement learning in multi-energy system case studies—0
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Benchmark Results

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