mirror of
https://github.com/vale981/ray
synced 2025-03-10 05:16:49 -04:00
173f1d629a
Remove rllib dep: trainable is now a standalone abstract class that can be easily subclassed. Clean up hyperband: fix debug string and add an example. Remove YAML api / ScriptRunner: this was never really used. Move ray.init() out of run_experiments(): This provides greater flexibility and should be less confusing since there isn't an implicit init() done there. Note that this is a breaking API change for tune.
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import ray
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from ray.tune import grid_search, register_env, run_experiments
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from env import CarlaEnv, ENV_CONFIG
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from models import register_carla_model
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from scenarios import TOWN2_STRAIGHT
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env_name = "carla_env"
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env_config = ENV_CONFIG.copy()
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env_config.update({
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"verbose": False,
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"x_res": 80,
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"y_res": 80,
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"squash_action_logits": grid_search([False, True]),
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"use_depth_camera": False,
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"discrete_actions": False,
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"server_map": "/Game/Maps/Town02",
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"reward_function": grid_search(["custom", "corl2017"]),
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"scenarios": TOWN2_STRAIGHT,
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})
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register_env(env_name, lambda env_config: CarlaEnv(env_config))
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register_carla_model()
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redis_address = ray.services.get_node_ip_address() + ":6379"
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ray.init(redis_address=redis_address)
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run_experiments({
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"carla-a3c": {
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"run": "A3C",
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"env": "carla_env",
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"resources": {"cpu": 5, "gpu": 2, "driver_gpu_limit": 0},
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"config": {
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"env_config": env_config,
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"use_gpu_for_workers": True,
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"model": {
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"custom_model": "carla",
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"custom_options": {
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"image_shape": [80, 80, 6],
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},
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"conv_filters": [
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[16, [8, 8], 4],
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[32, [4, 4], 2],
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[512, [10, 10], 1],
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],
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},
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"gamma": 0.95,
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"num_workers": 2,
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},
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},
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})
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