52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
import sys
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from legged_gym import LEGGED_GYM_ROOT_DIR
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import os
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import sys
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from legged_gym import LEGGED_GYM_ROOT_DIR
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import isaacgym
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from legged_gym.envs import *
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from legged_gym.utils import get_args, export_policy_as_jit, task_registry, Logger
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import numpy as np
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import torch
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def play(args):
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env_cfg, train_cfg = task_registry.get_cfgs(name=args.task)
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# override some parameters for testing
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env_cfg.env.num_envs = min(env_cfg.env.num_envs, 100)
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env_cfg.terrain.num_rows = 5
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env_cfg.terrain.num_cols = 5
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env_cfg.terrain.curriculum = False
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env_cfg.noise.add_noise = False
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env_cfg.domain_rand.randomize_friction = False
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env_cfg.domain_rand.push_robots = False
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env_cfg.env.test = True
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# prepare environment
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env, _ = task_registry.make_env(name=args.task, args=args, env_cfg=env_cfg)
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obs = env.get_observations()
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# load policy
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train_cfg.runner.resume = True
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ppo_runner, train_cfg = task_registry.make_alg_runner(env=env, name=args.task, args=args, train_cfg=train_cfg)
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policy = ppo_runner.get_inference_policy(device=env.device)
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# export policy as a jit module (used to run it from C++)
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if EXPORT_POLICY:
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path = os.path.join(LEGGED_GYM_ROOT_DIR, 'logs', train_cfg.runner.experiment_name, 'exported', 'policies')
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export_policy_as_jit(ppo_runner.alg.actor_critic, path)
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print('Exported policy as jit script to: ', path)
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for i in range(10*int(env.max_episode_length)):
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actions = policy(obs.detach())
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obs, _, rews, dones, infos = env.step(actions.detach())
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if __name__ == '__main__':
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EXPORT_POLICY = True
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RECORD_FRAMES = False
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MOVE_CAMERA = False
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args = get_args()
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play(args)
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