Reinforcement learning using OpenAI Gym has the ability to make a reinforcement model for playing Super Mario Bros. I tried doing this following Nicholas Renotte's youtube tutorial but around 10 minutes I get the errors "too many values to unpack (expected 4) or "not enough values to unpack (expected 5, got 4)."
The error comes from the 4 parameter return in the loop, but I think it originates from where "env" is instantiated.
From Jupyter Notebook:
#!pip install gym_super_mario_bros==7.3.0 nes_py
import gym_super_mario_bros #import game
from nes_py.wrappers import JoypadSpace #import wrapper
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT #import basic movements
# Initialize the game
env = gym_super_mario_bros.make('SuperMarioBros-v0', apply_api_compatibility=True, render_mode="human")
#env = gym_super_mario_bros.make('SuperMarioBros-v0')
#make calls the type of environment.you can find more environmnets on the gym website.
print(env.action_space) #this shows there are 256 actions (complex)
env = JoypadSpace(env, SIMPLE_MOVEMENT)
#this wraps the environmnet with the simple movement inputs into one object
print(env.action_space) #This shows there are 7 available actions (simplified)
print(env.observation_space.shape)
print(env.observation_space)
print((env.action_space.sample()))
done = True # Create a flag when finished to know when to restart
for step in range(100000): # Loop through each frame in the game
if done:
# Start the gamee
env.reset()
state, reward, done, info = env.step(env.action_space.sample())
# Do random actions
# Show the game on the screen
env.render()
# Close the game
env.close()
The issue is with this line : state, reward, done, info = env.step(env.action_space.sample()). you're trying to unpack env.step using 4 variables instead of 5. Take a look at the documentation of the step function here
Replace it with this :
state, reward, done, truncated , info = env.step(env.action_space.sample()