Comments (2)
Pretrained agents for now just follow a simple guideline. They should be able to perform well on the environment at hand and gain a stable reward. To tune my environments, I need pretrained agents which perform well on the original mujoco envs so that I can tune my replicas to work similarly.
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I see that you are pretty quick at training agents. Would you be interested in training some agents on the original mujoco environments to help me tune my replicas here? I am happy about agents trained in any framework, for now I prefer tensorforce since I have it set up already, but if it is easier for you to train in another framework, that is fine.
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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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