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 5976–6000 of 15113 papers

TitleStatusHype
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