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 5526–5550 of 15113 papers

TitleStatusHype
Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation—0
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training—0
Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World—0
Robust Reinforcement Learning for Autonomous Driving—0
Robust Reinforcement Learning for Continuous Control with Model Misspecification—0
Robust Reinforcement Learning in POMDPs with Incomplete and Noisy Observations—0
Robust Reinforcement Learning on Graphs for Logistics optimization—0
Robust Reinforcement Learning through Efficient Adversarial Herding—0
Robust Reinforcement Learning under Diffusion Models for Data with Jumps—0
Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel—0
Robust Reinforcement Learning via Genetic Curriculum—0
Robust Reinforcement Learning with Distributional Risk-averse formulation—0
Robust Reinforcement Learning with Wasserstein Constraint—0
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning—0
Robust Risk-Aware Option Hedging—0
Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets—0
Robust Risk-Sensitive Reinforcement Learning with Conditional Value-at-Risk—0
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving—0
Robust Spoken Language Understanding with RL-based Value Error Recovery—0
Robust synchronization and policy adaptation for networked heterogeneous agents—0
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning—0
Role Playing Learning for Socially Concomitant Mobile Robot Navigation—0
ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation—0
RoMFAC: A robust mean-field actor-critic reinforcement learning against adversarial perturbations on states—0
Room Clearance with Feudal Hierarchical Reinforcement Learning—0
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Benchmark Results

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