LightFM
gym
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| LightFM | gym | |
|---|---|---|
| 0 | 66 | |
| 4,068 | 27,971 | |
| 0.8% | 1.0% | |
| 6.2 | 9.5 | |
| 4 months ago | 1 day ago | |
| Python | Python | |
| MIT License | MIT License |
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LightFM
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Tracking mentions began in Dec 2020.
gym
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[P] The Fast Deep Reinforcement Learning Course
This course (the first in a planned multi-part series) shows how to use the Deep Reinforcement Learning framework RLlib to solve OpenAI Gym environments. I provide a big-picture overview of RL and show how to use the tools to get the job done. This approach is similar to learning Deep Learning by building and training various deep networks using a high-level framework e.g. Keras.
- Is it possible to use the MuJoCo Gym environments with the new Python binding ?
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Installing & Using MuJoCo 2.1.5 with OpenAi Gym
We are making mujoco installation a lot easier (i.e. pip install gym[mujoco]) without all the pains with mujoco-py by adopting Deepmind’s new mujoco bindings https://github.com/openai/gym/pull/2762, but this is a work in progress…
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Simulating random RGB images and observation space for RL model
And according to the source code, the frame stack returns the most recent observations.
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Changing the observation space from real valued quantities to visual obs
I don't know if it is seamlessly compatible with this specific environment, but in general you can use the PixelObservationWrapper for this type of thing (with pixels_only=False). https://github.com/openai/gym/blob/master/gym/wrappers/pixel_observation.py
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Is it possible to modify the reward function during training of an agent using OpenAI/Stable-Baselines3?
I would recommend doing this using an environment RewardWrapper. Here you have an example https://github.com/openai/gym/blob/master/gym/wrappers/transform_reward.py
- Where is env.nS for Frozen Lake in OpenAI Gym
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openAI gym return done==True but not seeing goal is reached
See https://github.com/openai/gym/issues/2510
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In gym can i play the exact same game again with the exact same states?
You need to be more specific. Even at a high level OpenAI Gym is just a wrapper and how the environment state is implemented will vary between something like Atari and MuJoCo. Here is an old but relevant issue on the GitHub.
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OpenAI gym: Lunar Lander V2 Question
In the Rewards section of the source code, "Firing the main engine is -0.3 points each frame." and "If the lander crashes, it receives an additional -100 points."
What are some alternatives?
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