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TAXONS
0.1
Task Agnostic eXploration of Outcome spaces through Novelty and Surprise
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Classes | |
| class | BaseAgent |
| class | DMPAgent |
| class | FFNeuralAgent |
Variables | |
| agent = DMPAgent({'degree':5, 'dof':1, 'type':'poly'}) | |
| env = gym.make('Billiard-v0') | |
| list | a = [] |
| list | b = [] |
| int | ts = 1000 |
| f = agent(k) | |
| fig = plt.figure() | |
| ax1 = fig.add_subplot(111) | |
| list taxons.core.evolution.agents.a = [] |
| taxons.core.evolution.agents.agent = DMPAgent({'degree':5, 'dof':1, 'type':'poly'}) |
| taxons.core.evolution.agents.ax1 = fig.add_subplot(111) |
| list taxons.core.evolution.agents.b = [] |
| taxons.core.evolution.agents.env = gym.make('Billiard-v0') |
| taxons.core.evolution.agents.f = agent(k) |
| taxons.core.evolution.agents.fig = plt.figure() |
| int taxons.core.evolution.agents.ts = 1000 |