import torch
from universe import World
class Amir(torch.nn.Module):
"""Research Intern @ OIST"""
def __init__(self):
super().__init__()
self.interests = ["DL", "RL", "World Models", "Robotics", "Embodied AI", "Computational Biology"]
self.optimizer = AdamW(self.parameters(), lr=3e-4) # Karpathy's constant
self.epsilon = 0.3 # still in exploration phase
def forward(self, obs: Tensor) -> Action:
z = self.world_model(obs) # compress reality into latent space
return self.policy(z) # act
def loss(self) -> Tensor:
return (
curiosity(weight=1.0)
+ depth(weight=0.8)
- stagnation(weight=float("inf"))
)
def step(self):
self.loss().backward()
self.optimizer.step()
self.optimizer.zero_grad()
