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 12951–13000 of 15113 papers

TitleStatusHype
Trust Region-Guided Proximal Policy OptimizationCode0
Multi-Agent Reinforcement Learning with Multi-Step Generative Models—0
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networksCode0
A Regulation Enforcement Solution for Multi-agent Reinforcement Learning—0
Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning—0
CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments—0
Reward Shaping via Meta-Learning—0
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning—0
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift—0
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning—0
Action Robust Reinforcement Learning and Applications in Continuous ControlCode0
Emergent Linguistic Phenomena in Multi-Agent Communication GamesCode0
Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization—0
Learning agile and dynamic motor skills for legged robotsCode1
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based roboticsCode0
Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning—0
Dynamic Measurement Scheduling for Event Forecasting using Deep RLCode0
Federated Deep Reinforcement Learning—0
Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems—0
Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning—0
Reinforcement Learning of Markov Decision Processes with Peak Constraints—0
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) CompetitionCode0
Hierarchical Reinforcement Learning for Multi-agent MOBA Game—0
Distillation Strategies for Proximal Policy Optimization—0
Causal Reasoning from Meta-reinforcement LearningCode0
Towards Learning to Imitate from a Single Video Demonstration—0
Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN TargetCode0
Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning—0
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture SearchCode0
A Short Survey on Probabilistic Reinforcement Learning—0
Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in VideosCode0
Towards Physically Safe Reinforcement Learning under Supervision—0
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems—0
On-Policy Trust Region Policy Optimisation with Replay BuffersCode0
WALL-E: An Efficient Reinforcement Learning Research FrameworkCode0
Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning—0
Amplifying the Imitation Effect for Reinforcement Learning of UCAV's Mission ExecutionCode0
Evolutionarily-Curated Curriculum Learning for Deep Reinforcement Learning Agents—0
Representation Learning on Graphs: A Reinforcement Learning Application—0
Transfer Learning for Prosthetics Using Imitation LearningCode0
Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement LearningCode0
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning—0
Comparing Knowledge-based Reinforcement Learning to Neural Networks in a Strategy Game—0
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
Improving Coordination in Small-Scale Multi-Agent Deep Reinforcement Learning through Memory-driven CommunicationCode0
An investigation of model-free planningCode0
On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator—0
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning—0
Motion Perception in Reinforcement Learning with Dynamic Objects—0
A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cutting—0
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Benchmark Results

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