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https://github.com/Farama-Foundation/Gymnasium.git
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Change end-of-episode in CarRacing to termination as opposed to truncation (#813)
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@@ -127,7 +127,7 @@ In order to wrap an environment, you must first initialize a base environment. T
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```python
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>>> import gymnasium as gym
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>>> from gymnasium.wrappers import FlattenObservation
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>>> env = gym.make("CarRacing-v2")
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>>> env = gym.make("CarRacing-v3")
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>>> env.observation_space.shape
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(96, 96, 3)
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>>> wrapped_env = FlattenObservation(env)
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@@ -156,7 +156,7 @@ If you have a wrapped environment, and you want to get the unwrapped environment
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```python
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>>> wrapped_env
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<FlattenObservation<TimeLimit<OrderEnforcing<PassiveEnvChecker<CarRacing<CarRacing-v2>>>>>>
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<FlattenObservation<TimeLimit<OrderEnforcing<PassiveEnvChecker<CarRacing<CarRacing-v3>>>>>>
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>>> wrapped_env.unwrapped
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<gymnasium.envs.box2d.car_racing.CarRacing object at 0x7f04efcb8850>
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```
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@@ -115,7 +115,7 @@ register(
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)
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register(
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id="CarRacing-v2",
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id="CarRacing-v3",
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entry_point="gymnasium.envs.box2d.car_racing:CarRacing",
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max_episode_steps=1000,
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reward_threshold=900,
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@@ -153,9 +153,9 @@ class CarRacing(gym.Env, EzPickle):
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```python
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>>> import gymnasium as gym
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>>> env = gym.make("CarRacing-v2", render_mode="rgb_array", lap_complete_percent=0.95, domain_randomize=False, continuous=False)
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>>> env = gym.make("CarRacing-v3", render_mode="rgb_array", lap_complete_percent=0.95, domain_randomize=False, continuous=False)
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>>> env
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<TimeLimit<OrderEnforcing<PassiveEnvChecker<CarRacing<CarRacing-v2>>>>>
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<TimeLimit<OrderEnforcing<PassiveEnvChecker<CarRacing<CarRacing-v3>>>>>
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```
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@@ -176,7 +176,7 @@ class CarRacing(gym.Env, EzPickle):
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```python
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>>> import gymnasium as gym
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>>> env = gym.make("CarRacing-v2", domain_randomize=True)
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>>> env = gym.make("CarRacing-v3", domain_randomize=True)
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# normal reset, this changes the colour scheme by default
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>>> obs, _ = env.reset()
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@@ -190,6 +190,7 @@ class CarRacing(gym.Env, EzPickle):
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```
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## Version History
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- v2: Change truncation to termination when finishing the lap (1.0.0)
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- v1: Change track completion logic and add domain randomization (0.24.0)
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- v0: Original version
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@@ -564,6 +565,7 @@ class CarRacing(gym.Env, EzPickle):
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step_reward = 0
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terminated = False
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truncated = False
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info = {}
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if action is not None: # First step without action, called from reset()
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self.reward -= 0.1
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# We actually don't want to count fuel spent, we want car to be faster.
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@@ -572,18 +574,18 @@ class CarRacing(gym.Env, EzPickle):
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step_reward = self.reward - self.prev_reward
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self.prev_reward = self.reward
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if self.tile_visited_count == len(self.track) or self.new_lap:
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# Truncation due to finishing lap
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# This should not be treated as a failure
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# but like a timeout
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truncated = True
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# Termination due to finishing lap
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terminated = True
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info["lap_finished"] = True
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x, y = self.car.hull.position
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if abs(x) > PLAYFIELD or abs(y) > PLAYFIELD:
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terminated = True
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info["lap_finished"] = False
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step_reward = -100
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if self.render_mode == "human":
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self.render()
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return self.state, step_reward, terminated, truncated, {}
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return self.state, step_reward, terminated, truncated, info
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def render(self):
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if self.render_mode is None:
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@@ -212,7 +212,7 @@ def play(
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>>> import gymnasium as gym
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>>> import numpy as np
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>>> from gymnasium.utils.play import play
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>>> play(gym.make("CarRacing-v2", render_mode="rgb_array"), # doctest: +SKIP
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>>> play(gym.make("CarRacing-v3", render_mode="rgb_array"), # doctest: +SKIP
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... keys_to_action={
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... "w": np.array([0, 0.7, 0], dtype=np.float32),
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... "a": np.array([-1, 0, 0], dtype=np.float32),
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@@ -309,7 +309,7 @@ class FrameStackObservation(
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Example:
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>>> import gymnasium as gym
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>>> from gymnasium.wrappers import FrameStackObservation
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>>> env = gym.make("CarRacing-v2")
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>>> env = gym.make("CarRacing-v3")
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>>> env = FrameStackObservation(env, stack_size=4)
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>>> env.observation_space
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Box(0, 255, (4, 96, 96, 3), uint8)
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@@ -225,7 +225,7 @@ class FlattenObservation(
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Example:
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>>> import gymnasium as gym
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>>> from gymnasium.wrappers import FlattenObservation
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>>> env = gym.make("CarRacing-v2")
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>>> env = gym.make("CarRacing-v3")
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>>> env.observation_space.shape
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(96, 96, 3)
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>>> env = FlattenObservation(env)
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@@ -267,7 +267,7 @@ class GrayscaleObservation(
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Example:
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>>> import gymnasium as gym
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>>> from gymnasium.wrappers import GrayscaleObservation
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>>> env = gym.make("CarRacing-v2")
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>>> env = gym.make("CarRacing-v3")
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>>> env.observation_space.shape
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(96, 96, 3)
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>>> grayscale_env = GrayscaleObservation(env)
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@@ -345,7 +345,7 @@ class ResizeObservation(
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Example:
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>>> import gymnasium as gym
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>>> from gymnasium.wrappers import ResizeObservation
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>>> env = gym.make("CarRacing-v2")
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>>> env = gym.make("CarRacing-v3")
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>>> env.observation_space.shape
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(96, 96, 3)
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>>> resized_env = ResizeObservation(env, (32, 32))
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@@ -416,7 +416,7 @@ class ReshapeObservation(
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Example:
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>>> import gymnasium as gym
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>>> from gymnasium.wrappers import ReshapeObservation
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>>> env = gym.make("CarRacing-v2")
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>>> env = gym.make("CarRacing-v3")
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>>> env.observation_space.shape
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(96, 96, 3)
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>>> reshape_env = ReshapeObservation(env, (24, 4, 96, 1, 3))
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@@ -213,7 +213,7 @@ class FlattenObservation(VectorizeTransformObservation):
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Example:
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>>> import gymnasium as gym
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>>> envs = gym.make_vec("CarRacing-v2", num_envs=3, vectorization_mode="sync")
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>>> envs = gym.make_vec("CarRacing-v3", num_envs=3, vectorization_mode="sync")
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>>> obs, info = envs.reset(seed=123)
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>>> obs.shape
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(3, 96, 96, 3)
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@@ -238,7 +238,7 @@ class GrayscaleObservation(VectorizeTransformObservation):
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Example:
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>>> import gymnasium as gym
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>>> envs = gym.make_vec("CarRacing-v2", num_envs=3, vectorization_mode="sync")
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>>> envs = gym.make_vec("CarRacing-v3", num_envs=3, vectorization_mode="sync")
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>>> obs, info = envs.reset(seed=123)
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>>> obs.shape
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(3, 96, 96, 3)
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@@ -266,7 +266,7 @@ class ResizeObservation(VectorizeTransformObservation):
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Example:
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>>> import gymnasium as gym
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>>> envs = gym.make_vec("CarRacing-v2", num_envs=3, vectorization_mode="sync")
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>>> envs = gym.make_vec("CarRacing-v3", num_envs=3, vectorization_mode="sync")
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>>> obs, info = envs.reset(seed=123)
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>>> obs.shape
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(3, 96, 96, 3)
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@@ -292,7 +292,7 @@ class ReshapeObservation(VectorizeTransformObservation):
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Example:
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>>> import gymnasium as gym
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>>> envs = gym.make_vec("CarRacing-v2", num_envs=3, vectorization_mode="sync")
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>>> envs = gym.make_vec("CarRacing-v3", num_envs=3, vectorization_mode="sync")
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>>> obs, info = envs.reset(seed=123)
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>>> obs.shape
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(3, 96, 96, 3)
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@@ -242,7 +242,7 @@ def test_make_human_rendering(register_rendering_testing_envs):
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TypeError,
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match=re.escape("got an unexpected keyword argument 'render'"),
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):
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gym.make("CarRacing-v2", render="human")
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gym.make("CarRacing-v3", render="human")
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# This test checks that a user can create an environment without the metadata including the render mode
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with pytest.warns(
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@@ -54,7 +54,7 @@ def test_carracing_domain_randomize():
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CarRacing DomainRandomize should have different colours at every reset.
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However, it should have same colours when `options={"randomize": False}` is given to reset.
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"""
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env: CarRacing = gym.make("CarRacing-v2", domain_randomize=True).unwrapped
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env: CarRacing = gym.make("CarRacing-v3", domain_randomize=True).unwrapped
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road_color = env.road_color
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bg_color = env.bg_color
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@@ -68,7 +68,7 @@ def test_update_running_mean_property():
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def test_normalize_obs_with_vector():
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def thunk():
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env = gym.make("CarRacing-v2")
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env = gym.make("CarRacing-v3")
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env = gym.wrappers.GrayscaleObservation(env)
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env = gym.wrappers.NormalizeObservation(env)
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return env
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@@ -47,7 +47,7 @@ def test_resize_observation_wrapper(env):
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@pytest.mark.parametrize("shape", ((10, 10), (20, 20), (60, 60), (100, 100)))
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def test_resize_shapes(shape: tuple[int, int]):
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env = ResizeObservation(gym.make("CarRacing-v2"), shape)
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env = ResizeObservation(gym.make("CarRacing-v3"), shape)
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assert env.observation_space == Box(
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low=0, high=255, shape=shape + (3,), dtype=np.uint8
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)
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@@ -59,7 +59,7 @@ def test_resize_shapes(shape: tuple[int, int]):
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def test_invalid_input():
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env = gym.make("CarRacing-v2")
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env = gym.make("CarRacing-v3")
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with pytest.raises(AssertionError):
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ResizeObservation(env, ())
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@@ -42,9 +42,9 @@ def custom_environments():
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(
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("CustomDictEnv-v0", "FilterObservation", {"filter_keys": ["a"]}),
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("CartPole-v1", "FlattenObservation", {}),
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("CarRacing-v2", "GrayscaleObservation", {}),
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("CarRacing-v2", "ResizeObservation", {"shape": (35, 45)}),
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("CarRacing-v2", "ReshapeObservation", {"shape": (96, 48, 6)}),
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("CarRacing-v3", "GrayscaleObservation", {}),
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("CarRacing-v3", "ResizeObservation", {"shape": (35, 45)}),
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("CarRacing-v3", "ReshapeObservation", {"shape": (96, 48, 6)}),
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(
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"CartPole-v1",
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"RescaleObservation",
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@@ -53,7 +53,7 @@ def custom_environments():
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"max_obs": np.array([1, np.inf, 1, np.inf]),
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},
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),
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("CarRacing-v2", "DtypeObservation", {"dtype": np.int32}),
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("CarRacing-v3", "DtypeObservation", {"dtype": np.int32}),
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# ("CartPole-v1", "RenderObservation", {}), # not implemented
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# ("CartPole-v1", "TimeAwareObservation", {}), # not implemented
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# ("CartPole-v1", "FrameStackObservation", {}), # not implemented
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