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 8301–8325 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
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Benchmark Results

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