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 44914500 of 15113 papers

TitleStatusHype
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking0
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning0
A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications0
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to RealityCode4
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds0
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement LearningCode1
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning0
Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real DataCode1
Teal: Learning-Accelerated Optimization of WAN Traffic EngineeringCode1
Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture SearchCode0
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Benchmark Results

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