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 8251–8275 of 15113 papers

TitleStatusHype
A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless SystemsCode1
Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments—0
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning—0
Return-based Scaling: Yet Another Normalisation Trick for Deep RL—0
Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty—0
Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge GraphsCode1
Spectral Normalisation for Deep Reinforcement Learning: an Optimisation PerspectiveCode1
Efficient Self-Supervised Data Collection for Offline Robot Learning—0
Adaptive Policy Transfer in Reinforcement Learning—0
Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees—0
A Deep Reinforcement Learning Approach to Audio-Based Navigation in a Multi-Speaker EnvironmentCode0
Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning—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
Reinforcement Learning with Expert Trajectory For Quantitative Trading—0
Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge—0
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-ResolutionCode1
A parallel-network continuous quantitative trading model with GARCH and PPO—0
RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning—0
Scalable, Decentralized Multi-Agent Reinforcement Learning Methods Inspired by Stigmergy and Ant Colonies—0
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic CandidatesCode1
Deep reinforcement learning-designed radiofrequency waveform in MRICode1
Using reinforcement learning to design an AI assistantfor a satisfying co-op experience—0
Utilizing Skipped Frames in Action Repeats via Pseudo-Actions—0
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Benchmark Results

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