updated examples to run in benchmark

examples/custom_railmap_example.py

@@ -7,7 +7,7 @@ from flatland.core.grid.rail_env_grid import RailEnvTransitions
 from flatland.core.transition_map import GridTransitionMap
 from flatland.envs.rail_env import RailEnv
 from flatland.envs.rail_generators import RailGenerator, RailGeneratorProduct
-from flatland.envs.schedule_generators import ScheduleGenerator, compute_max_episode_steps
+from flatland.envs.schedule_generators import ScheduleGenerator
 from flatland.envs.schedule_utils import Schedule
 from flatland.utils.rendertools import RenderTool
 
@@ -31,15 +31,14 @@ def custom_rail_generator() -> RailGenerator:
 
 
 def custom_schedule_generator() -> ScheduleGenerator:
-    def generator(rail: GridTransitionMap, num_agents: int, hints: Any = None) -> Schedule:
+    def generator(rail: GridTransitionMap, num_agents: int, hints: Any = None,
+                  num_resets: int = 0) -> Schedule:
         agents_positions = []
         agents_direction = []
         agents_target = []
         speeds = []
-        max_episode_steps = compute_max_episode_steps(width=rail.width, height=rail.height)
         return Schedule(agent_positions=agents_positions, agent_directions=agents_direction,
-                        agent_targets=agents_target, agent_speeds=speeds, agent_malfunction_rates=None,
-                        max_episode_steps=max_episode_steps)
+                        agent_targets=agents_target, agent_speeds=speeds, agent_malfunction_rates=None)
 
     return generator
 

examples/debugging_example_DELETE.py

-import random
-import time
-from typing import List
-
-import numpy as np
-
-from flatland.core.env_observation_builder import ObservationBuilder
-from flatland.core.grid.grid4_utils import get_new_position
-from flatland.envs.rail_env import RailEnv
-from flatland.envs.rail_generators import complex_rail_generator
-from flatland.envs.schedule_generators import complex_schedule_generator
-from flatland.utils.rendertools import RenderTool
-
-random.seed(1)
-np.random.seed(1)
-
-
-class SingleAgentNavigationObs(ObservationBuilder):
-    """
-    We build a representation vector with 3 binary components, indicating which of the 3 available directions
-    for each agent (Left, Forward, Right) lead to the shortest path to its target.
-    E.g., if taking the Left branch (if available) is the shortest route to the agent's target, the observation vector
-    will be [1, 0, 0].
-    """
-
-    def __init__(self):
-        super().__init__()
-
-    def reset(self):
-        pass
-
-    def get(self, handle: int = 0) -> List[int]:
-        agent = self.env.agents[handle]
-
-        possible_transitions = self.env.rail.get_transitions(*agent.position, agent.direction)
-        num_transitions = np.count_nonzero(possible_transitions)
-
-        # Start from the current orientation, and see which transitions are available;
-        # organize them as [left, forward, right], relative to the current orientation
-        # If only one transition is possible, the forward branch is aligned with it.
-        if num_transitions == 1:
-            observation = [0, 1, 0]
-        else:
-            min_distances = []
-            for direction in [(agent.direction + i) % 4 for i in range(-1, 2)]:
-                if possible_transitions[direction]:
-                    new_position = get_new_position(agent.position, direction)
-                    min_distances.append(self.env.distance_map.get()[handle, new_position[0], new_position[1], direction])
-                else:
-                    min_distances.append(np.inf)
-
-            observation = [0, 0, 0]
-            observation[np.argmin(min_distances)] = 1
-
-        return observation
-
-
-env = RailEnv(width=14,
-              height=14,
-              rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=5, max_dist=99999, seed=1),
-              schedule_generator=complex_schedule_generator(),
-              number_of_agents=2,
-              obs_builder_object=SingleAgentNavigationObs())
-
-obs, info = env.reset()
-env_renderer = RenderTool(env, gl="PILSVG")
-env_renderer.render_env(show=True, frames=True, show_observations=False)
-for step in range(100):
-    actions = {}
-    for i in range(len(obs)):
-        actions[i] = np.argmax(obs[i]) + 1
-
-    if step % 5 == 0:
-        print("Agent halts")
-        actions[0] = 4  # Halt
-
-    obs, all_rewards, done, _ = env.step(actions)
-    if env.agents[0].malfunction_data['malfunction'] > 0:
-        print("Agent 0 broken-ness: ", env.agents[0].malfunction_data['malfunction'])
-
-    env_renderer.render_env(show=True, frames=True, show_observations=False)
-    time.sleep(0.5)
-    if done["__all__"]:
-        break
-env_renderer.close_window()