Imitation learning holds tremendous promise in learning policies efficiently for complex decision... more Imitation learning holds tremendous promise in learning policies efficiently for complex decision making problems. Current state-of-the-art algorithms often use inverse reinforcement learning (IRL), where given a set of expert demonstrations, an agent alternatively infers a reward function and the associated optimal policy. However, such IRL approaches often require substantial online interactions for complex control problems. In this work, we present Regularized Optimal Transport (ROT), a new imitation learning algorithm that builds on recent advances in optimal transport based trajectory-matching. Our key technical insight is that adaptively combining trajectory-matching rewards with behavior cloning can significantly accelerate imitation even with only a few demonstrations. Our experiments on 20 visual control tasks across the DeepMind Control Suite, the OpenAI Robotics Suite, and the Meta-World Benchmark demonstrate an average of 7.8× faster imitation to reach 90% of expert performance compared to prior state-of-the-art methods. On real-world robotic manipulation, with just one demonstration and an hour of online training, ROT achieves an average success rate of 90.1% across 14 tasks.
The world needs diverse and unbiased data to train deep learning models. Currently data comes fro... more The world needs diverse and unbiased data to train deep learning models. Currently data comes from a variety of sources that are unmoderated to a large extent. The outcomes of training neural networks with unverified data yields biased models with various strains of homophobia, sexism and racism. Another trend observed in the world of deep learning is the rise of distributed training. Although cloud companies provide high performance compute for training models in the form of GPU's connected with a low latency network, using these services comes at a high cost. We propose Hydra, a system that seeks to solve both of these problems in a novel manner by proposing a decentralized distributed framework which utilizes the substantial amount of idle compute of everyday electronic devices like smartphones and desktop computers for training and data collection purposes. Hydra couples a specialized distributed training framework on a network of these low powered devices with a reward sche...
The world needs diverse and unbiased data to train deep learning models. Currently data comes fro... more The world needs diverse and unbiased data to train deep learning models. Currently data comes from a variety of sources that are unmoderated to a large extent. The outcomes of training neural networks with unverified data yields biased models with various strains of homophobia, sexism and racism. Another trend observed in the world of deep learning is the rise of distributed training. Although cloud companies provide high performance compute for training models in the form of GPU's connected with a low latency network, using these services comes at a high cost. We propose Hydra, a system that seeks to solve both of these problems in a novel manner by proposing a decentralized distributed framework which utilizes the substantial amount of idle compute of everyday electronic devices like smartphones and desktop computers for training and data collection purposes. Hydra couples a specialized distributed training framework on a network of these low powered devices with a reward sche...
The world needs diverse and unbiased data to train deep learning models. Currently data comes fro... more The world needs diverse and unbiased data to train deep learning models. Currently data comes from a variety of sources that are unmoderated to a large extent. The outcomes of training neural networks with unverified data yields biased models with various strains of homophobia, sexism and racism. Another trend observed in the world of deep learning is the rise of distributed training. Although cloud companies provide high performance compute for training models in the form of GPU's connected with a low latency network, using these services comes at a high cost. We propose Hydra, a system that seeks to solve both of these problems in a novel manner by proposing a decentralized distributed framework which utilizes the substantial amount of idle compute of everyday electronic devices like smartphones and desktop computers for training and data collection purposes. Hydra couples a specialized distributed training framework on a network of these low powered devices with a reward sche...
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Papers by Vaibhav Mathur