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Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation
2021
Machine Learning
AbstractWe introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies' performance
doi:10.1007/s10994-021-06006-6
fatcat:zvrkocl7arb7vpdnlulhd3gb7u