Hey everyone, I’m struggling with a problem when trying to train a basic CartPole environment using TensorFlow and OpenAI Gym. I set up the environment and my agent, but during training I encounter an AttributeError that says ‘NoneType’ object has no attribute ‘set_current’.
Here’s a simplified version of my code:
import tensorflow as tf
import gym
import numpy as np
env = gym.make('CartPole-v1')
class SimpleAgent:
def __init__(self, state_size, action_size):
self.model = tf.keras.Sequential([
tf.keras.layers.Dense(24, activation='relu', input_shape=(state_size,)),
tf.keras.layers.Dense(24, activation='relu'),
tf.keras.layers.Dense(action_size, activation='softmax')
])
self.model.compile(optimizer='adam', loss='categorical_crossentropy')
agent = SimpleAgent(env.observation_space.shape[0], env.action_space.n)
for episode in range(100):
state = env.reset()
for step in range(500):
env.render() # This line causes the error
action = env.action_space.sample()
next_state, reward, done, _ = env.step(action)
if done:
break
env.close()
I’m running TensorFlow 2.x and the latest version of OpenAI Gym on Windows 10 with an Intel i5 processor and 16GB of RAM. Any suggestions on what might be causing this error? Thanks!
I’ve encountered similar issues when working with OpenAI Gym on Windows. One solution that worked for me was updating my graphics drivers. Sometimes outdated drivers can cause conflicts with the rendering process. Additionally, you might want to check if you have the necessary dependencies installed, particularly pyglet, which Gym uses for rendering. If the problem persists, consider using a virtual environment to isolate your project dependencies. This can help prevent conflicts between different versions of libraries. Lastly, if you’re not tied to using Gym, you might want to explore alternatives like PettingZoo or Unity ML-Agents, which can offer more robust support across different operating systems.
I’ve encountered similar issues with rendering in reinforcement learning setups and found that explicitly setting the backend can resolve compatibility problems. One effective method is to initialize pygame before creating the Gym environment. For instance, try the following approach:
import gym
import pygame
pygame.init()
env = gym.make(‘CartPole-v1’, render_mode=‘human’)
This adjustment allowed my training process to run smoothly on Windows. If the issue persists, consider experimenting with alternative visualization libraries like matplotlib, as they may offer more stability depending on your configuration. The key is to ensure your agent’s training isn’t interrupted by rendering problems.
hey lucask, looks like ur havin trouble with the render() function. on windows, openai gym can be finicky with rendering. try runnin ur code without the env.render() line and see if it works. if u really need visuals, maybe look into other rendering options or use a different OS. good luck!
yo lucask, sounds like a pain. have u tried updatin ur gym package? older versions can cause weird errors like that. also, check if ur using the right tensorflow version for ur gym. if nothin works, maybe try runnin it on a linux VM or somethin. hope this helps!
In my experience, these issues with rendering on Windows can be traced to how the Gym’s render() function interacts with the OS. One workaround that worked was to use headless rendering by specifying a render mode. For example, you can create your environment like this:
env = gym.make('CartPole-v1', render_mode='rgb_array')
This way, you avoid the UI-related errors by not trying to display the environment directly. Also, consider refining your training loop so that your agent actively learns from its interactions, which can help stabilize performance over multiple episodes. It’s a helpful approach to balance debugging and learning strategy.