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 10651–10675 of 15113 papers

TitleStatusHype
Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem—0
Reinforcement Learning based Design of Linear Fixed Structure Controllers—0
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning—0
Accelerating Deep Neuroevolution on Distributed FPGAs for Reinforcement Learning Problems—0
ALLSTEPS: Curriculum-driven Learning of Stepping Stone SkillsCode1
Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python—0
Synthesizing Safe Policies under Probabilistic Constraints with Reinforcement Learning and Bayesian Model Checking—0
Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients—0
Learning hierarchical behavior and motion planning for autonomous drivingCode1
Reinforcement Learning with Feedback Graphs—0
SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document SummarizationCode1
Plan2Vec: Unsupervised Representation Learning by Latent PlansCode1
Curious Hierarchical Actor-Critic Reinforcement LearningCode1
CARL: Controllable Agent with Reinforcement Learning for Quadruped LocomotionCode1
Adaptive Dialog Policy Learning with Hindsight and User Modeling—0
Safe Reinforcement Learning through Meta-learned Instincts—0
Robotic Arm Control and Task Training through Deep Reinforcement Learning—0
Gifting in multi-agent reinforcement learningCode0
Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection—0
A Survey on Dialog Management: Recent Advances and Challenges—0
Discrete-to-Deep Supervised Policy LearningCode0
Generalized Planning With Deep Reinforcement Learning—0
Formal Policy Synthesis for Continuous-Space Systems via Reinforcement Learning—0
Generalized Reinforcement Meta Learning for Few-Shot Optimization—0
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation—0
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Benchmark Results

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