8 papers with code • 1 benchmarks • 1 datasets
We leverage this scaling to train an agent for 2. 5 Billion steps of experience (the equivalent of 80 years of human experience) -- over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs.
Ranked #1 on PointGoal Navigation on Gibson PointGoal Navigation
The use of learning provides flexibility with respect to input modalities (in the SLAM module), leverages structural regularities of the world (in global policies), and provides robustness to errors in state estimation (in local policies).
We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of over 19, 000 frames of experience per second on a single GPU and up to 72, 000 frames per second on a single eight-GPU machine.
PointGoal Navigation is an embodied task that requires agents to navigate to a specified point in an unseen environment.
In this work, we detail how to transfer the knowledge acquired in simulation into the real world.