Comments (6)
Hi! That was the original intent of the reimplementations! From the observations and actions length point of view, they should be compatible actually. Checkout the HumanoidMuJoCoEnv-v0, the other is the roboschool one that is oddly different (no idea why they did that). In case they are not equal in length, tell me. Another issue I have for now is that I have no idea about the corresponding observations between mujoco and pybullet. Mujoco has a cryptic state vector of the environment and it is hard to find out what the openai guys did there. So I have a hard time to get the right observations.
I am very interested in pretrained agents for all openai gym mujoco envs btw such that I can test the similarity of my envs to the openai gym reference implementation. If you want to help me with any of this, that would be great!
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On the chance that the port works: I have no idea actually. For now it only worked with the pendulums to be honest. For now I am stuck with the observations such that I can not really tell yet.
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Hi again,
so I managed to port the HumanoidFlugrun to MuJoCo. I would like to try to train the same ppo agent you trained for yours. However, the Tensorforce has evolved significantly and it looks like the API isn't the same. Could you tell me the command you used and the version of Tensorforce?
Thanks a lot.
Lukas
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Hi all,
I'm currently working on making some bullet environments that are compatible with the OpenAI Mujoco ones. I started making my own before discovering this project.
I am very interested in pretrained agents for all openai gym mujoco envs btw such that I can test the similarity of my envs to the openai gym reference implementation. If you want to help me with any of this, that would be great!
I am currently conducting the same experiments basically, with both your environments and the ones I started. I have some polices that work well in mujoco, but I have not gotten them to transfer successfully yet. I've been focusing on walker2d first (for no particular reason) I see two big problems to the transfer.
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I think there is some mismatches going on in the state vectors. There are some things like the starting height being 0 in the bullet env vs 1.25 in the mujoco one, and I think the joint ordering is different between the two. This kind of stuff will be tedious but easy to fix.
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The physics are different between the two simulators. I've been using pybullets setPhysicsParameters and setDynamicsParameters to get as close as possible to Mujoco. the two sims have fundamentally different constraint models so I'm not sure how close we can get, or if being policy compatible is possible.
I'm not sure if any of you are working on this anymore (this thread is rather old now), but if so I'm happy to share what I have.
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Related Issues (20)
- pybullet build error on macos 10.15.5 HOT 1
- Why are there references to extra "non-existent" links? HOT 2
- The bulit-in texture doesn't load correctly HOT 2
- HopperMuJoCoEnv HOT 7
- Feature request / Would like to contribute: Competitive Environments
- Rendering (mode='rgb_array') error
- How to Cite PyBullet-Gym HOT 2
- How to get the timestep of an environment? HOT 2
- How to set the environment status manually? HOT 2
- Recommended way to change the robot mass HOT 1
- Stopping render - relation between the simulation and rendering HOT 1
- AntMuJoCoEnv-v0 contains many unnecessary states. HOT 13
- robot out of view when using env.render('rgb_array')
- how to connect two arms' end-effectors
- New environment request: OpenAI's Safety Gym
- Python kernal crashing HOT 4
- apparent environment seed issues with ant environment
- Any suggestions on how to lock certain joints?
- AttributeError: 'HopperBulletEnv' object has no attribute '_p'
- Which version of gym does this lib support? HOT 1
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