Pieter Abbeel Team Proposes Task-Agnostic RL Method to Auto-Tune Simulations to the Real World | Synced
A research team from UC Berkeley and Carnegie Mellon University proposes a task-agnostic reinforcement learning method that reduces the task-specific engineering required for domain randomization o...
Source: Synced | AI Technology & Industry Review
A research team from UC Berkeley and Carnegie Mellon University proposes a task-agnostic reinforcement learning method that reduces the task-specific engineering required for domain randomization of both visual and dynamics parameters.