Fully observability about minigrid HOT 4 CLOSED

Hmm, first thing is, you should be calling env.render('rgb_array') (which will return a numpy array), not env.render('human'), and this should be done within the wrapper's observation method.

For speed, the simplest thing to do would be to render a smaller version of the grid. But the most efficient way would be to encode the grid directly as a numpy array with 3 values per cell as I did for partial observability. See the encode method of the Grid class: https://github.com/maximecb/gym-minigrid/blob/master/gym_minigrid/minigrid.py#L508

The only downside of this is that it doesn't currently encode the agent position.

from minigrid.

Thank you for the quick reply. Something like this should the trick then:

    def observation(self, obs):
        full_grid = self.env.grid.encode()
        full_grid[self.env.agent_pos[0]][self.env.agent_pos[1]] = self.env.agent_dir
        return full_grid

from minigrid.

Yes, like that, except you'd want to also encode that the agent is at that position, not just the agent direction. Something like:

full_grid[x, y, 0] = 255
full_grid[x, y, 1] = self.env.agent_dir
full_grid[x, y, 2] = 0

You would also want to change the observation_space to have the correct shape.

from minigrid.

For anyone using this: I recommend encoding the agent as a much lower number that is closer to how the other objects are encoded.

While training on the Unlock-environment converged in ~12 minutes with the regular, egocentric view, training with the FullyObsWrapper never converged. At first I thought the egocentric view gives a translation invariance that the FullyObsWrapper doesn't have, so I tried to compensate and make the environment much simpler by removing colors, giving a small reward when picking up the key or reducing the action space such that pickup and toggle were reduced to 'interact' and drop was removed. Despite all of this, the model just wouldn't learn to go to the door after picking up the key.

Finally I changed the agent encoding from 255 to 9, and now training with the fully observable view converges as fast as with the egocentric view. Possibly the high value is too dominant in the convnet's processing.

PS. The current FullyObsWrapper also doesn't encode which item is being carried. In the egocentric view, this is encoded by showing the item as if it is at the agent's position, but the FullyObsWrapper overwrites this grid position to encode the agent. If color is not important, you can encode the carried object's type at the agent's position in the 3rd layer. If color is important, you will need to add a 4th layer to also include the carried object's color.

from minigrid.