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 5951–6000 of 15113 papers

TitleStatusHype
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning—0
Learning in Mean Field Games: A Survey—0
Learning medical triage from clinicians using Deep Q-Learning—0
Learning Memory-Dependent Continuous Control from Demonstrations—0
Data-Driven Merton's Strategies via Policy Randomization—0
Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning—0
Learning Mobile Robot Navigation in the Dense Crowd with Deep Reinforcement Learning—0
Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer—0
Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach—0
Learning Montezuma's Revenge from a Single Demonstration—0
Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance—0
Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning—0
Learning Natural Language Generation from Scratch—0
Learning Navigation Behaviors End-to-End with AutoRL—0
Learning Near Optimal Policies with Low Inherent Bellman Error—0
Learning Not to Spoof—0
Learning objects from pixels—0
Learning offline: memory replay in biological and artificial reinforcement learning—0
Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration—0
Learning on Abstract Domains: A New Approach for Verifiable Guarantee in Reinforcement Learning—0
Learning Online Policies for Person Tracking in Multi-View Environments—0
Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions—0
Learning Open Domain Multi-hop Search Using Reinforcement Learning—0
Learning Optimal Deterministic Policies with Stochastic Policy Gradients—0
Learning Optimal Strategies for Temporal Tasks in Stochastic Games—0
Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space—0
Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning—0
Learning Options from Demonstration using Skill Segmentation—0
Learning over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System—0
Learning Parsimonious Dynamics for Generalization in Reinforcement Learning—0
Learning Partially Observable Deterministic Action Models—0
Learning Perception-Aware Agile Flight in Cluttered Environments—0
Learning Personalized Discretionary Lane-Change Initiation for Fully Autonomous Driving Based on Reinforcement Learning—0
Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study—0
Learning Pessimism for Robust and Efficient Off-Policy Reinforcement Learning—0
Learning Physics Priors for Deep Reinforcement Learing—0
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV—0
Learning Policy Representations in Multiagent Systems—0
Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations—0
Learning Power Control from a Fixed Batch of Data—0
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning—0
Learning Predictive Communication by Imagination in Networked System Control—0
Learning predictive representations in autonomous driving to improve deep reinforcement learning—0
Learning Predictive Safety Filter via Decomposition of Robust Invariant Set—0
Inferring Probabilistic Reward Machines from Non-Markovian Reward Processes for Reinforcement Learning—0
Learning proposals for sequential importance samplers using reinforced variational inference—0
Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents—0
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning—0
Learning Quadruped Locomotion Policies using Logical Rules—0
Learning Realistic Traffic Agents in Closed-loop—0
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Benchmark Results

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