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BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay
Thus, we propose BeTAIL: Behavior Transformer Adversarial Imitation Learning, which combines a Behavior Transformer (BeT) policy from human demonstrations with online AIL.
Improving Wind Resistance Performance of Cascaded PID Controlled Quadcopters using Residual Reinforcement Learning
Wind resistance control is an essential feature for quadcopters to maintain their position to avoid deviation from target position and prevent collisions with obstacles.
Skill-Critic: Refining Learned Skills for Reinforcement Learning
Hierarchical reinforcement learning (RL) can accelerate long-horizon decision-making by temporally abstracting a policy into multiple levels.
Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation
We demonstrate that the proposed method performs expert human-level vehicle control under high-speed driving scenarios even with game screen images as high-dimensional inputs.
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