[Question] - Why different results each time loaded model is run?

[snafu4]

Any ideas why the code below (it is basically the same as the code in the README.md file) would return different results each time it is run after the model has been trained? The trained model is no longer being updated and the input (trades) do not change between runs.

` model = A2C.load(modelName, env)

observation = env.reset()
while True:
    action, _states = model.predict(observation)
    observation, reward, done, info = env.step(action)

    state = env.render()
    z = calcsomevalue(state)
    if done:
       x  = getsomevalue(z)
       y = calsomevalue(z)
        print(<results>)
        break

`

[snafu4]

clarification of my question

import gym
from gym_mtsim import (
    Timeframe, SymbolInfo,
    MtSimulator, OrderType, Order, SymbolNotFound, OrderNotFound,
    MtEnv,
    FOREX_DATA_PATH, STOCKS_DATA_PATH, CRYPTO_DATA_PATH, MIXED_DATA_PATH,
)
from stable_baselines3 import A2C

env = gym.make('forex-hedge-v0')

model = A2C('MultiInputPolicy', env, verbose=0)
model.learn(total_timesteps=1000)

observation = env.reset()
while True:
    action, _states = model.predict(observation)
    observation, reward, done, info = env.step(action)
    if done:
        break

modelName = 'model_test'
model.save(f'{modelName}.zip')

env.render('advanced_figure', time_format="%Y-%m-%d")

### 
# running the loop below should not result in different results each time, should it...
# ... but it does!  What am I missing here?
### 
model = A2C.load(modelName)

for i in range(5):
    observation = env.reset()
    while True:
        action, _states = model.predict(observation)
        observation, reward, done, info = env.step(action)
        if done:
            break

    env.render('advanced_figure', time_format="%Y-%m-%d")
'''

[snafu4]

model.predict(observation, deterministic=True) appears to fix the problem

see https://stable-baselines.readthedocs.io/en/master/guide/rl_tips.html for specifics