[Question] Algorithm / Parameters for Ant Maze

[meppe]

Question

I tried hard to train an agent to solve any of the AntMaze environments. I tried the stable baselines 3 implementations of SAC (dense and sparse) and PPO, but could not solve even a small open AntMaze environment. I tried random goal and starting position and fixed positions. Has anyone successfully trained an agent one of the maze envs so far? If so what were the parameters and algorithm used, and which variant of the environment?

What happens a lot in my case is that the Ant flips upside-down. It cannot walk on its "elbows" in that position, so that it remains stuck.

Thanks a lot! Manfred (Hamburg University of Technology, Germany)

[Kallinteris-Andreas]

Hey,

it sounds like your agent is incapable of locomotion, can you solve PointEnv?, can you locomote the Ant environment by itself?

@rodrigodelazcano may know which algorithms can solve it

[meppe]

Hi, yes, PointMaze solves really well.

However, I have to add that I changed the reward a bit. I remember that SAC with sparse rewards and HER went really well with the Ant4Rooms environment as in the hierarchical actor-critic paper by Levy et al https://www.youtube.com/watch?v=TY3gr4SRmPk&ab_channel=AndrewLevy This was originally for hierarchical actor-critic but went fine without hierarcical approaches. There, the reward was from -1 (goal not reached) to 0 (goal reached).

After I found that your original version of AntMaze with the original reward function did not work with SAC, no matter if sparse or dense rewards, I tried the reward [-1,0], but without improvement. Also, I played around with continuing_task and reset_target parameters, etc. no success in any configuration. I usually stop training after 500k steps, which should be more than sufficient.

My team's general goal is to add a working config for AntMaze to our Scilab-RL framework you can find here: https://scilab-rl.github.io/Scilab-RL/

Any hint is appreciated! Best, Manfred! M,

[meppe]

Oh, and yes, the locomotion itself works. At least, sometimes, and only until it flips over. Here are some videos: https://api.wandb.ai/links/manfred-eppe/avqvho1o

[Kallinteris-Andreas]

Hey, I am familiar with this trained locomotion behavior of the Ant model from the Gymnasium/MuJoCo/Ant environment (also happens to other quadrupeds robots, from my testing)

Where:

• the Ant falls over
• the Ant Jumps really high and fails to land back on its feet

This was addressed in Gymnasium/MuJoCo/Ant environment by adding the healthy_z_range behavior, which adds a negative reward and terminated the environment when the ant falls over or jumps really high. https://gymnasium.farama.org/main/environments/mujoco/ant/#termination

You could try implementing that by creating a wrapper or forking the environment class.

[Kallinteris-Andreas]

You could also check minari's datasets