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Updated README

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Dominik Moritz Roth 2022-09-21 12:11:58 +02:00
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README.md
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@ -29,15 +29,12 @@ There exist two ways to implement new envs:
- If you want visualize the covariance you have supply the cholesky-decomp of the cov-matrix to render - If you want visualize the covariance you have supply the cholesky-decomp of the cov-matrix to render
- If you want to render into a mp4, you have to call render with a mode!='human' and assemble/encode the returned frames yourself into a mp4/webm/... - If you want to render into a mp4, you have to call render with a mode!='human' and assemble/encode the returned frames yourself into a mp4/webm/...
- Even while the agent plays, some keyboard-inputs are possible (to test the agents reaction to situations he would never enter by itself. Look at \_handle_user_input in env.py for avaible keys) - Even while the agent plays, some keyboard-inputs are possible (to test the agents reaction to situations he would never enter by itself. Look at \_handle_user_input in env.py for avaible keys)
- The sampling-rate of the physics engine is bound to the frame-rate of the rendering engine (1:1). This means too low fps / to fast agents / to thin barriers will lead to the agent tunneling through barriers. You can fix this by setting a higher agent-drag (which decreases the maximum speed) or making barriers thicker. A feature allowing the physics engine to sample multiple smaler steps within a single rendering step could be added in the future.
### entities.py ### entities.py
Contains all implemented entities (e.g. the Agent, Rewards and Enemies) Contains all implemented entities (e.g. the Agent, Rewards and Enemies)
##### Some caveats
- Support for non spherical entities (rectangles) is very new. There might be bugs that I have not yet found
### observables.py ### observables.py
Contains all 'oberservables'. These are attached to envs to define what kind of output is given to the agent. This way environments can be designed independently from the observation machanism that is used by the agent to play it. Contains all 'oberservables'. These are attached to envs to define what kind of output is given to the agent. This way environments can be designed independently from the observation machanism that is used by the agent to play it.
@ -45,11 +42,8 @@ Contains all 'oberservables'. These are attached to envs to define what kind of
##### Some caveats ##### Some caveats
- CNNObservable seems to be broken currently. (Fixing it is also no priority for me) - CNNObservable seems to be broken currently. (Fixing it is also no priority for me)
- RayObservable is using a naive ray-marching (basicaly just line-sweeping). For large amounts of rays this turn out to be the computational bottleneck of the environment. Switching to a more efficient algorithm (based on euclidean formulars and line intersects) would be possible in the future...
### humanPlayer.py ### humanPlayer.py
Allows environments to be played by a human using mouse input. Allows environments to be played by a human using mouse input. Now even works for ColumbusConfigDefined.
##### Some caveats
- Does not yet work for ColumbusConfigDefined...