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https://github.com/elsa-lab/gym-deepracer
A simple AWS DeepRacer simulator using Unity with OpenAI gym interface.
https://github.com/elsa-lab/gym-deepracer
aws deepracer openai-gym unity
Last synced: 11 days ago
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A simple AWS DeepRacer simulator using Unity with OpenAI gym interface.
- Host: GitHub
- URL: https://github.com/elsa-lab/gym-deepracer
- Owner: elsa-lab
- Created: 2019-07-15T07:23:15.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-07-15T07:25:15.000Z (over 5 years ago)
- Last Synced: 2024-11-11T06:34:02.728Z (2 months ago)
- Topics: aws, deepracer, openai-gym, unity
- Language: Python
- Homepage:
- Size: 18 MB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DeepRacer Gym
## Param List
* More info: [AWS DeepRacer Developer Guide](https://docs.aws.amazon.com/en_us/deepracer/latest/developerguide/deepracer-reward-function-input.html)
```
{
"all_wheels_on_track": Boolean, # flag to indicate if the vehicle is on the track
"x": float, # vehicle's x-coordinate in meters
"y": float, # vehicle's y-coordinate in meters
"distance_from_center": float, # distance in meters from the track center
"is_left_of_center": Boolean, # Flag to indicate if the vehicle is on the left side to the track center or not.
"heading": float, # vehicle's yaw in degrees
"progress": float, # percentage of track completed.
"steps": int, # number steps completed
"speed": float, # vehicle's speed in meters per second (m/s)
"steering_angle": float, # vehicle's steering angle in degrees
"track_width": float, # width of the track
"waypoints": [[float, float], … ], # list of [x,y] as milestones along the track center
"closest_waypoints": [int, int] # indices of the two nearest waypoints.
}
```
## Example 1
```python
import os
import sys
import time
import logging
from DeepRacer_gym import CustomRewardWrapper
from DeepRacer_gym import DeepRacerActionWrapper
import DeepRacer_gym as deepracer
LOG = logging.getLogger()
# Define your custom reward function
def reward_fn(params):
'''
Example of using all_wheels_on_track and speed
'''
# Read input variables
all_wheels_on_track = params['all_wheels_on_track']
speed = params['speed']
# Set the speed threshold based your action space
SPEED_THRESHOLD = 1.0
if not all_wheels_on_track:
# Penalize if te car goes off track
reward = 1e-3
elif speed < SPEED_THRESHOLD:
# Penalize if the car goes too slow
reward = 0.5
else:
# High reward if the car stays on track and goes fast
reward = 1.0
return reward
# Create environment
env = deepracer.make('NewYorkCity-v0')
env = CustomRewardWrapper(env, reward_fn)
env = DeepRacerActionWrapper(env, max_steering_angle = 30,
steering_angle_granularity = 5,
max_speed = 3,
speed_granularity = 3)
# Print action space info
print(env.action_space)
action_table = env.action_table()
# Print action table
print('Action number\t\tSteering\t\tSpeed')
for t in action_table:
print('{}\t\t\t{}\t\t\t{}'.format(t['Action number'], t['Steering'], t['Speed']))
MAXIMUM_STEPS = 1000
state = env.reset()
for step in range(MAXIMUM_STEPS):
action = env.action_space.sample() # random sample
state, reward, done, info = env.step(action)
LOG.info("[step {:4d}] action: ({:6.2f}, {:6.2f}), speed: {:10.6f}, steering: {:10.2f}, xy: ({:10.6f}, {:10.6f}), all_wheels_on_track: {}, closest_waypoints: {}".format(
info['steps'], action_table[action]['Speed'], action_table[action]['Steering'], info['speed'], info['steering_angle'], info['x'], info['y'], info['all_wheels_on_track'], info['closest_waypoints']))
env.close()
```
## Example 2: Integrating with stable_baselines
```python
import os
import sys
import time
import logging
from DeepRacer_gym import CustomRewardWrapper
from DeepRacer_gym import DeepRacerActionWrapper
import DeepRacer_gym as deepracer
from stable_baselines import PPO2
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines.common.policies import MlpPolicy
# Define your custom reward function
def reward_fn(params):
'''
Example of using all_wheels_on_track and speed
'''
# Read input variables
all_wheels_on_track = params['all_wheels_on_track']
speed = params['speed']
# Set the speed threshold based your action space
SPEED_THRESHOLD = 1.0
if not all_wheels_on_track:
# Penalize if te car goes off track
reward = 1e-3
elif speed < SPEED_THRESHOLD:
# Penalize if the car goes too slow
reward = 0.5
else:
# High reward if the car stays on track and goes fast
reward = 1.0
return reward
# Create environment
env = deepracer.make('NewYorkCity-v0')
env = CustomRewardWrapper(env, reward_fn)
env = DeepRacerActionWrapper(env, max_steering_angle = 30,
steering_angle_granularity = 5,
max_speed = 3,
speed_granularity = 3)
MAX_TRAINING_STEPS = 5000
MAX_EVALUATE_STEPS = 1000
# Create Dummy Env
env = DummyVecEnv([lambda: env])
model = PPO2(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=MAX_TRAINING_STEPS)
states = env.reset()
for step in range(MAX_EVALUATE_STEPS):
action, _states = model.predict(states)
state, reward, done, info = env.step(action)
if info['progress'] >= 99.99:
print("Track complete")
env.close()
```
## Example 3: stable_baselines SubprocVecEnv
* [example3.py](https://github.com/Ending2015a/DeepRacer_gym/blob/master/Examples/example3.py)
## Environments
* `NewYorkCity-v0`
## Update Environments
1. Remove all the files in `DeepRacer_gym/envs/new_york_city/executable` directory
2. Download/Clone latest version of `env_info.py` from the repository and place in `DeepRacer_gym/envs/new_york_city/env_info.py`