This repository contains model weights for the agents performing in RoadEnv.

Models

• Recurrent Soft Actor-Critic (RSAC/SAC-LSTM) [Agent] [Training] [Test]
• Recurrent Soft Actor-Critic Share (RSAC-Share) [Paper] [Agent] [Training]
• Soft Actor-Critic (SAC) [Agent] [Training] [Test]

Usage

# Register environment
from road_env import register_road_envs
register_road_envs()

# Make environment
import gymnasium as gym
env = gym.make('urban-road-v0', render_mode='rgb_array')

# Configure parameters (example)
env.configure({
    "random_seed": None,
    "duration": 60,
})

obs, info = env.reset()

# Graphic display
import matplotlib.pyplot as plt
plt.imshow(env.render())

# Execution
done = truncated = False
while not (done or truncated):
    action = ... # Your agent code here
    obs, reward, done, truncated, info = env.step(action)
    env.render() # Update graphic