silverwingsbot--easycarla-rl
wehub-resource-sync
82 行
2.6 KiB
Python
82 行
2.6 KiB
Python
"""
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Author: SilverWings
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GitHub: https://github.com/silverwingsbot
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This script provides a minimal demo to interact with the EasyCarla-RL environment.
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It follows the standard Gym interface (reset, step) and demonstrates basic environment usage.
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"""
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import gym
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import easycarla
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import carla
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import random
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import numpy as np
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# Configure environment parameters
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params = {
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'number_of_vehicles': 100,
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'number_of_walkers': 0,
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'dt': 0.1, # time interval between two frames
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'ego_vehicle_filter': 'vehicle.tesla.model3', # filter for defining ego vehicle
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'surrounding_vehicle_spawned_randomly': True, # Whether surrounding vehicles are spawned randomly (True) or set manually (False)
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'port': 2000, # connection port
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'town': 'Town03', # which town to simulate
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'max_time_episode': 1000, # maximum timesteps per episode
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'max_waypoints': 12, # maximum number of waypoints
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'visualize_waypoints': True, # Whether to visualize waypoints (default: True)
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'desired_speed': 8, # desired speed (m/s)
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'max_ego_spawn_times': 200, # maximum times to spawn ego vehicle
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'view_mode' : 'top', # 'top' for bird's-eye view, 'follow' for third-person view
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'traffic': 'off', # 'on' for normal traffic lights, 'off' for always green and frozen
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'lidar_max_range': 50.0, # Maximum LIDAR perception range (meters)
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'max_nearby_vehicles': 5, # Maximum number of nearby vehicles to observe
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}
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# Create the environment
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env = gym.make('carla-v0', params=params)
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obs = env.reset()
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# Define a simple action policy
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def get_action(env, obs):
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"""Randomly choose either a simple manual action or an autopilot action."""
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p = random.random()
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if p < 0.5:
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# Use autopilot (Expert mode)
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env.ego.set_autopilot(True)
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control = env.ego.get_control()
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action = [control.throttle, control.steer, control.brake]
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else:
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# Use random action (Novice mode)
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env.ego.set_autopilot(False)
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throttle = random.uniform(0.0, 1.0)
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steer = random.uniform(-0.6, 0.6)
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brake = random.uniform(0.0, 0.3)
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action = [throttle, steer, brake]
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return action
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# Interact with the environment
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for episode in range(5): # Run 5 episodes
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obs = env.reset()
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done = False
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total_reward = 0
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while not done:
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action = get_action(env, obs)
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next_obs, reward, cost, done, info = env.step(action)
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print(f"Step: {env.time_step}, Reward: {reward:.2f}, Cost: {cost:.2f}, Done: {done}")
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obs = next_obs
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total_reward += reward
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print(f"Episode {episode} finished. Total reward: {total_reward:.2f}")
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env.close()
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