def __init__(self):
super().__init__(screenWidth, screenHeight, "Sumo")
file_path = os.path.dirname(os.path.abspath(__file__))
os.chdir(file_path)
arcade.set_background_color(arcade.color.GRAY_BLUE)
self.a = sumo.arena.Arena((screenWidth, screenHeight))
self.tDown = False
self.speedMode = False
self.bot0LeftMotor = 0.0
self.bot0RightMotor = 0.0
self.bot0Boost = False
self.bot1LeftMotor = 0.0
self.bot1RightMotor = 0.0
self.bot1Boost = False
########################### Create Agents ###########################
# Sensors: arena distance from center, angle relative to center, distance from opponent, angle to opponent, angle between opponents, boost
# Actions: Left motor, right motor, boost
self.cs = pyswarm.PyComputeSystem(16)
numInputs = 6
numActions = 3
numLayers = 2
inputSize = pyswarm.PyInt3(1, 1, numInputs)
lds = []
for i in range(numLayers):
l = pyswarm.PyLayerDesc()
l._layerType = "conv"
l._filterRadius = 0
l._numMaps = numActions if i == numLayers - 1 else 24
l._recurrent = False if i == numLayers - 1 else True
l._actScalar = 6.0
lds.append(l)
self.h0 = pyswarm.PyHierarchy(self.cs, inputSize, lds, 32)
self.h1 = pyswarm.PyHierarchy(self.cs, inputSize, lds, 32)
self.h0.setOptAlpha(0.001)
self.h1.setOptAlpha(0.001)
self.rewardNoise = 0.0
import pyswarm
import numpy as np
import gym
env = gym.make('CartPole-v0')
numInputs = env.observation_space.shape[0]
cs = pyswarm.PyComputeSystem(16, seed=np.random.randint(0, 99999))
inputSize = pyswarm.PyInt3(1, 1, numInputs)
lds = []
numLayers = 2
for i in range(numLayers):
l = pyswarm.PyLayerDesc()
l._layerType = "conv"
l._filterRadius = 0 # Dense
l._numMaps = 16 if i < numLayers - 1 else 1
l._recurrent = i < numLayers - 1
l._actScalar = 6.0
lds.append(l)
h = pyswarm.PyHierarchy(cs, inputSize, lds, 32)
h.setOptAlpha(0.01)
import pyswarm
import numpy as np
import gym
import gym_ple
import lycon
from copy import copy
import scipy.misc
env = gym.make('Catcher-v0')
imageSize = (16, 16)
numActions = env.action_space.n
cs = pyswarm.PyComputeSystem(16)
inputSize = pyswarm.PyInt3(imageSize[0], imageSize[1], 1)
lds = []
numLayers = 5
for i in range(numLayers):
l = pyswarm.PyLayerDesc()
if i % 2 == 0:
l._layerType = "conv"
l._filterRadius = 2
l._numMaps = 4 + i * 2 if i < numLayers - 1 else numActions
l._recurrent = True if i == numLayers - 2 else False
import pyswarm
import numpy as np
import gym
env = gym.make('BipedalWalker-v2')
numInputs = env.observation_space.shape[0]
numActions = env.action_space.shape[0]
cs = pyswarm.PyComputeSystem(16, 1234)
inputSize = pyswarm.PyInt3(1, 1, numInputs)
lds = []
numLayers = 2
for i in range(numLayers):
l = pyswarm.PyLayerDesc()
l._layerType = "conv"
l._filterRadius = 0 # Dense
l._numMaps = 16 if i != numLayers - 1 else numActions
l._recurrent = i != numLayers - 1
l._actScalar = 6.0
lds.append(l)
h = pyswarm.PyHierarchy(cs, inputSize, lds, 32)