def test_bn():
cfg = ConfigLoader.load_config(file_name="test_config.json")
input_shape = 8
output_shape = 8
instructions = cfg["policy"]
#parser = MiscParsingFunctions.parse_policy_action_function(cfg["policy"]["action_parser"])
policy = FeedForward(input_shape, output_shape, None, cfg)
policy.build_model(instructions)
print("BUILT POLICY LAYERS:")
for layer in policy.model:
print(layer)
inp = np.ones(input_shape)
output = policy.activate(inp)
print("POLICY OUTPUT ON ONES OF SHAPE", inp.shape, "=", output.shape,
"EXPECTED", output_shape)
action_parsers = {
"linear": parsers.linear_parse,
"random sample": parsers.random_sample,
"arg max": parsers.argmax_sample
}
for name, parser in action_parsers.items():
policy.action_parser = parser
print("ATTEMPTING PARSER", name)
print("RAW POLICY OUTPUT:", policy.activate(inp)[0])
print("SUM:", sum(policy.activate(inp)[0]))
action = policy.get_action(inp)
print("POLICY ACTION FROM PARSER", name, "=", action)
print()
def test_batch_bn():
cfg = ConfigLoader.load_config(file_name="test_config.json")
input_shape = 8
output_shape = 8
instructions = cfg["policy"]
parser = MiscParsingFunctions.parse_policy_action_function(
cfg["policy"]["action_parser"])
policy = FeedForward(input_shape, output_shape, parser, cfg)
policy.build_model(instructions)
print("BUILT POLICY LAYERS:")
for layer in policy.model:
print(layer)
batch = [np.ones(input_shape) for _ in range(500)]
output = policy.activate_batch(batch)
print("OUTPUT ON ONES BATCH OF 500:", output.shape, "EXPECTED 500 OF",
output_shape)
action_parsers = {
"linear": parsers.linear_parse,
"random sample": parsers.random_sample,
"arg max": parsers.argmax_sample
}
for name, parser in action_parsers.items():
policy.action_parser = parser
action = policy.get_actions_on_batch(batch)
print("POLICY ACTIONS FROM PARSER", name, "=", action)
print()