Init

legged_gym/scripts/play.py

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+import sys
+from legged_gym import LEGGED_GYM_ROOT_DIR
+import os
+import sys
+from legged_gym import LEGGED_GYM_ROOT_DIR
+
+import isaacgym
+from legged_gym.envs import *
+from legged_gym.utils import  get_args, task_registry, Logger
+from legged_gym.utils.exporter import export_policy_as_jit, export_policy_as_onnx, export_policy_as_pkl
+
+import numpy as np
+import torch
+
+def play(args):
+    env_cfg, train_cfg = task_registry.get_cfgs(name=args.task)
+    # override some parameters for testing
+    env_cfg.env.num_envs = min(env_cfg.env.num_envs, 100)
+    # env_cfg.terrain.mesh_type = 'plane'
+    env_cfg.terrain.num_rows = 7
+    env_cfg.terrain.num_cols = 7
+    env_cfg.terrain.curriculum = False
+    env_cfg.noise.add_noise = False
+    env_cfg.domain_rand.randomize_friction = False
+    env_cfg.domain_rand.push_robots = False
+    env_cfg.domain_rand.randomize_base_mass = False
+    env_cfg.domain_rand.randomize_link_mass = False
+    env_cfg.domain_rand.randomize_base_com = False
+    env_cfg.domain_rand.randomize_pd_gains = False
+    env_cfg.domain_rand.randomize_motor_zero_offset = False
+
+    env_cfg.env.test = True
+
+    # prepare environment
+    env, _ = task_registry.make_env(name=args.task, args=args, env_cfg=env_cfg)
+    obs = env.get_observations()
+    # load policy
+    train_cfg.runner.resume = True
+    runner, train_cfg = task_registry.make_alg_runner(env=env, name=args.task, args=args, train_cfg=train_cfg)
+    policy = runner.get_inference_policy(device=env.device)
+    
+    # export policy as a jit module (used to run it from C++)
+    if EXPORT_POLICY:
+        path = os.path.join(LEGGED_GYM_ROOT_DIR, 'logs', train_cfg.runner.experiment_name, 'exported', 'policies')
+        if hasattr(runner.alg, 'actor_critic'):
+            model = runner.alg.actor_critic
+        else:
+            model = runner.alg.model
+        export_policy_as_jit(model, path)
+        export_policy_as_onnx(model, path)
+        export_policy_as_pkl(model, path)
+        print('Exported policy as jit script / onnx to: ', path)
+
+    for i in range(10*int(env.max_episode_length)):
+        actions = policy(obs.detach())
+
+        if FIX_COMMAND:
+            env.commands[:, 0] = 1.0
+            env.commands[:, 1] = 0.0
+            env.commands[:, 2] = 0.0
+
+        obs, _, rews, dones, infos = env.step(actions.detach())
+
+if __name__ == '__main__':
+    EXPORT_POLICY = True
+    RECORD_FRAMES = False
+    MOVE_CAMERA = False
+    FIX_COMMAND = True
+    args = get_args()
+    play(args)

legged_gym/scripts/train.py

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+import os
+import numpy as np
+from datetime import datetime
+import sys
+
+import isaacgym
+from legged_gym.envs import *
+from legged_gym.utils import get_args, task_registry
+import torch
+
+def train(args):
+    env, env_cfg = task_registry.make_env(name=args.task, args=args)
+    runner, train_cfg = task_registry.make_alg_runner(env=env, name=args.task, args=args)
+    env.common_step_counter = runner.current_learning_iteration * env.num_steps_per_env  # resume env step counter
+    env.update_reward_curriculum(force_update=True)  # force update reward curriculum at start
+    runner.learn(num_learning_iterations=train_cfg.runner.max_iterations, init_at_random_ep_len=True)
+
+if __name__ == '__main__':
+    args = get_args()
+    train(args)