Better tests
alias sit="desk sit"
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83
test.py
83
test.py
@ -3,6 +3,7 @@ from time import sleep, time
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import numpy as np
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import pygame
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import yaml
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import random
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from columbus import env
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from columbus.observables import Observable, CnnObservable
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@ -13,11 +14,14 @@ from priorConditionedAnnealing import pca
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def main():
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agent_func = choosePlayType()
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env = chooseEnv()
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alg_name, agent_func = choosePlayType()
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env = chooseEnv(alg_name)
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while True:
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playEnv(env, agent_func)
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input('<again?>')
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try:
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agent_func.reset()
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except:
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pass
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env.close()
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@ -56,7 +60,7 @@ def loadConfigDefinedEnv(EnvClass):
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return EnvClass(fps=30, **cur)
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def chooseEnv():
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def chooseEnv(alg_name):
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envs = list(getAvaibleEnvs())
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for i, Env in enumerate(envs):
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print('['+str(i)+'] '+Env.__name__)
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@ -72,7 +76,7 @@ def chooseEnv():
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if envs[i] in [env.ColumbusConfigDefined]:
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return loadConfigDefinedEnv(envs[i])
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Env = envs[i]
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return Env(fps=30, agent_draw_path=True, path_decay=1/256)
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return Env(fps=30, agent_draw_path=True, path_decay=1/1024, title_appendix=' ['+alg_name+']', max_steps=30*10, clear_path_on_reset=False)
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def value_func(obs):
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@ -91,27 +95,33 @@ def human_input(obs, env):
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class Colored_Noise():
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def __init__(self, beta=1, dim_a=2, samples=2**18):
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self.beta = beta
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self.dim_a = dim_a
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self.samples = samples
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self.index = 0
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self.samples = cn.powerlaw_psd_gaussian(beta, (dim_a, samples))
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self.reset()
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def __call__(self, obs, env):
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sample = self.samples[:, self.index]
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self.index += 1
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return sample
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def reset(self):
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self.samples = cn.powerlaw_psd_gaussian(
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self.beta, (self.dim_a, self.samples), random_state=rand_seed())
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class PCA_Noise():
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def __init__(self, dim_a=2, kernel_func='SE_1.41_1', window=8, ssf=-1):
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self.dist = pca.PCA_Distribution(
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action_dim=dim_a, par_strength='CONT_DIAG', kernel_func=kernel_func, window=window)
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self.dist.proba_distribution(th.Tensor([[0]*2]), th.Tensor([[1]*2]))
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self.traj = [[0]*dim_a]
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self.ssf = 32
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def __init__(self, dim_a=2, kernel_func='SE_1.41_1', window=64, ssf=-1):
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self.dim_a = dim_a
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self.kernel_func = kernel_func
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self.window = window
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self.ssf = ssf
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self.index = 0
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self.reset()
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def __call__(self, obs, env):
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if self.index == self.ssf:
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self.index = 0
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if self.ssf != -1 and self.index % self.ssf == 0:
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self.traj = [[0]*len(self.traj[0])]
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traj = th.Tensor(self.traj).unsqueeze(0)
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sample = self.dist.sample(traj).squeeze(0)
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@ -119,10 +129,50 @@ class PCA_Noise():
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self.index += 1
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return sample
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def reset(self):
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self.dist = pca.PCA_Distribution(
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action_dim=self.dim_a, par_strength='CONT_DIAG', kernel_func=self.kernel_func, window=self.window)
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self.dist.proba_distribution(th.Tensor([[0]*2]), th.Tensor([[1]*2]))
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self.traj = [[0]*self.dim_a]
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class Colored_PCA_Noise():
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def __init__(self, beta=1, dim_a=2, samples=2**18, kernel_func='SE_1.41_1', window=64, ssf=-1):
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self.beta = beta
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self.dim_a = dim_a
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self.samples = samples
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self.kernel_func = kernel_func
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self.window = window
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self.ssf = ssf
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self.index = 0
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self.reset()
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def __call__(self, obs, env):
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epsilon = self.samples[:, self.index]
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if self.ssf != -1 and self.index % self.ssf == 0:
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self.traj = [[0]*len(self.traj[0])]
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traj = th.Tensor(self.traj).unsqueeze(0)
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sample = self.dist.sample(traj).squeeze(0)
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self.traj.append(sample)
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self.index += 1
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return sample
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def reset(self):
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self.dist = pca.PCA_Distribution(
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action_dim=self.dim_a, par_strength='CONT_DIAG', kernel_func=self.kernel_func, window=self.window)
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self.dist.proba_distribution(th.Tensor([[0]*2]), th.Tensor([[1]*2]))
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self.traj = [[0]*self.dim_a]
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self.samples = cn.powerlaw_psd_gaussian(
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self.beta, (self.dim_a, self.samples), random_state=rand_seed())
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def rand_seed():
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return int(99999999 * random.random())
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def choosePlayType():
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options = {'human': human_input, 'PCA': PCA_Noise(),
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'REX': Colored_Noise(beta=0), 'PINK': Colored_Noise(beta=1), 'BROWN': Colored_Noise(beta=2), 'BETA.5': Colored_Noise(beta=.5)}
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'REX': Colored_Noise(beta=0), 'PINK': Colored_Noise(beta=1), 'BROWN': Colored_Noise(beta=2), 'BETA.5': Colored_Noise(beta=.5), 'PINK_PCA': Colored_PCA_Noise(beta=1)}
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for i, name in enumerate(options):
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print('['+str(i)+'] '+name)
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while True:
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@ -136,7 +186,8 @@ def choosePlayType():
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print(
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'[!] That is a number, but not one that makes sense in this context...')
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else:
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return options[list(options.keys())[i]]
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name = list(options.keys())[i]
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return name, options[name]
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def playEnv(env, agent_func):
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