Daniel Salvadori danaugrs

## vulkaninfo3.txt
WARNING: [Loader Message] Code 0 : ReadDataFilesInRegistry: Registry lookup failed to get layer manifest files.
==========
VULKANINFO
==========

Vulkan Instance Version: 1.1.114


Instance Extensions:

## vulkaninfo2.txt
WARNING: [Loader Message] Code 0 : ReadDataFilesInRegistry: Registry lookup failed to get layer manifest files.
==========
VULKANINFO
==========

Vulkan Instance Version: 1.1.114


Instance Extensions:

## vulkaninfo.txt
==========
VULKANINFO
==========

Vulkan Instance Version: 1.1.114


Instance Extensions:
====================

## huskarl-parallel-environments-snippet.py
# We will be running multiple concurrent environment instances
instances = 16

# Create a policy for each instance with a different distribution for epsilon
policy = [hk.policy.Greedy()] + [hk.policy.GaussianEpsGreedy(eps, 0.1) for eps in np.arange(0, 1, 1/(instances-1))]

# Create Advantage Actor-Critic agent
agent = hk.agent.A2C(model, actions=dummy_env.action_space.n, nsteps=2, instances=instances, policy=policy)

# Create simulation, train and then test

## huskarl-minimal-dqn-cartpole.py
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import huskarl as hk
import gym

# Setup gym environment
create_env = lambda: gym.make('CartPole-v0').unwrapped
dummy_env = create_env()

# Build a simple neural network with 3 fully connected layers as our model
	WARNING: [Loader Message] Code 0 : ReadDataFilesInRegistry: Registry lookup failed to get layer manifest files.
	==========
	VULKANINFO
	==========

	Vulkan Instance Version: 1.1.114



	Instance Extensions:
	# We will be running multiple concurrent environment instances
	instances = 16

	# Create a policy for each instance with a different distribution for epsilon
	policy = [hk.policy.Greedy()] + [hk.policy.GaussianEpsGreedy(eps, 0.1) for eps in np.arange(0, 1, 1/(instances-1))]

	# Create Advantage Actor-Critic agent
	agent = hk.agent.A2C(model, actions=dummy_env.action_space.n, nsteps=2, instances=instances, policy=policy)

	# Create simulation, train and then test
	from tensorflow.keras.models import Sequential
	from tensorflow.keras.layers import Dense
	import huskarl as hk
	import gym

	# Setup gym environment
	create_env = lambda: gym.make('CartPole-v0').unwrapped
	dummy_env = create_env()

	# Build a simple neural network with 3 fully connected layers as our model