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limit += 1
if limit > 20:
break
return total_reward
def one_hot_encoding(probs):
one_hot = np.zeros_like(probs)
one_hot[:, np.argmax(probs, axis=1)] = 1
return one_hot
image_based = False
if image_based:
env = football_env.create_environment(env_name='academy_empty_goal', representation='pixels', render=True)
else:
env = football_env.create_environment(env_name='academy_empty_goal', representation='simple115')
state = env.reset()
state_dims = env.observation_space.shape
n_actions = env.action_space.n
dummy_n = np.zeros((1, 1, n_actions))
dummy_1 = np.zeros((1, 1, 1))
tensor_board = TensorBoard(log_dir='./logs')
if image_based:
model_actor = get_model_actor_image(input_dims=state_dims, output_dims=n_actions)
model_critic = get_model_critic_image(input_dims=state_dims)
else: