Instead of building world models into algorithms, we propose using large-scale multi-task world models as
differentiable simulators for policy learning. When well-regularized, these models enable efficient policy
learning with first-order gradient optimization. This allows PWM to learn to solve 80 tasks in < 10 minutes
each without the need for expensive online planning.
Structure of repository
pwm
├── dflex
│ ├── data - data used for dflex world model pre-training
│ └── pretrained - already trained world models that can be used in dflex experiments
├── multitask - pre-trained world models for multitask evaluation
├── pedagogical - pre-trained world models for recreating pedagogical examples
└── README.md
