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 9051–9075 of 15113 papers

TitleStatusHype
A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks—0
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control—0
Acting upon Imagination: when to trust imagined trajectories in model based reinforcement learning—0
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning—0
Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments—0
Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty—0
Return-based Scaling: Yet Another Normalisation Trick for Deep RL—0
Reinforcement learning of rare diffusive dynamics—0
Parameter-free Gradient Temporal Difference Learning—0
PEARL: Parallelized Expert-Assisted Reinforcement Learning for Scene Rearrangement Planning—0
Efficient Self-Supervised Data Collection for Offline Robot Learning—0
Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning—0
A Deep Reinforcement Learning Approach to Audio-Based Navigation in a Multi-Speaker EnvironmentCode0
Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees—0
Adaptive Policy Transfer in Reinforcement Learning—0
Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge—0
Reinforcement Learning with Expert Trajectory For Quantitative Trading—0
A parallel-network continuous quantitative trading model with GARCH and PPO—0
Scalable, Decentralized Multi-Agent Reinforcement Learning Methods Inspired by Stigmergy and Ant Colonies—0
RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning—0
Utilizing Skipped Frames in Action Repeats via Pseudo-Actions—0
Using reinforcement learning to design an AI assistantfor a satisfying co-op experience—0
Reward prediction for representation learning and reward shaping—0
Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learning—0
A Reinforcement Learning-based Economic Model Predictive Control Framework for Autonomous Operation of Chemical Reactors—0
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Benchmark Results

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