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

Showing 381390 of 15113 papers

TitleStatusHype
Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous VehiclesCode2
Visual Reinforcement Learning with Imagined GoalsCode2
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics ModelsCode2
Accelerated Methods for Deep Reinforcement LearningCode2
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement LearningCode2
Flow: A Modular Learning Framework for Mixed Autonomy TrafficCode2
Learning through Dialogue Interactions by Asking QuestionsCode2
Dialogue Learning With Human-In-The-LoopCode2
Benchmarking Deep Reinforcement Learning for Continuous ControlCode2
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement LearningCode2
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Benchmark Results

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