Started adding training similar to w-t-w

examples/isaacgym_train.py

@@ -5,6 +5,8 @@ import matplotlib.pyplot as plt
 import torch
 from tqdm import trange
 
+from ml_logger import logger
+
 from Go2Py.sim.gym.envs import *
 from Go2Py.sim.gym.envs.base.legged_robot_config import Cfg
 from Go2Py.sim.gym.envs.go2.go2_config import config_go2
@@ -195,12 +197,55 @@ def run_env(render=False, headless=False):
         print("Show the first frame and exit.")
         exit()
 
-    for i in trange(1000, desc="Running"):
+    # log the experiment parameters
-        actions = 0. * torch.ones(env.num_envs, env.num_actions, device=env.device)
+    logger.log_params(AC_Args=vars(AC_Args), PPO_Args=vars(PPO_Args), RunnerArgs=vars(RunnerArgs),
-        obs, rew, done, info = env.step(actions)
+                      Cfg=vars(Cfg))
-        # breakpoint()
+
-    print("Done")
+    env = HistoryWrapper(env)
+    gpu_id = 2
+    runner = Runner(env, device=f"cuda:{gpu_id}")
+    runner.learn(num_learning_iterations=10000, init_at_random_ep_len=True, eval_freq=100)
+
+    # for i in trange(1000, desc="Running"):
+    #     actions = 0. * torch.ones(env.num_envs, env.num_actions, device=env.device)
+    #     obs, rew, done, info = env.step(actions)
+    #     # breakpoint()
+    # print("Done")
 
 
 if __name__ == '__main__':
+    from pathlib import Path
+    from ml_logger import logger
+    from go2_gym import MINI_GYM_ROOT_DIR
+
+    stem = Path(__file__).stem
+    logger.configure(logger.utcnow(f'gait-conditioned-agility/%Y-%m-%d/{stem}/%H%M%S.%f'),
+                     root=Path(f"{MINI_GYM_ROOT_DIR}/runs").resolve(), )
+    logger.log_text("""
+                charts: 
+                - yKey: train/episode/rew_total/mean
+                  xKey: iterations
+                - yKey: train/episode/rew_tracking_lin_vel/mean
+                  xKey: iterations
+                - yKey: train/episode/rew_tracking_contacts_shaped_force/mean
+                  xKey: iterations
+                - yKey: train/episode/rew_action_smoothness_1/mean
+                  xKey: iterations
+                - yKey: train/episode/rew_action_smoothness_2/mean
+                  xKey: iterations
+                - yKey: train/episode/rew_tracking_contacts_shaped_vel/mean
+                  xKey: iterations
+                - yKey: train/episode/rew_orientation_control/mean
+                  xKey: iterations
+                - yKey: train/episode/rew_dof_pos/mean
+                  xKey: iterations
+                - yKey: train/episode/command_area_trot/mean
+                  xKey: iterations
+                - yKey: train/episode/max_terrain_height/mean
+                  xKey: iterations
+                - type: video
+                  glob: "videos/*.mp4"
+                - yKey: adaptation_loss/mean
+                  xKey: iterations
+                """, filename=".charts.yml", dedent=True)
     run_env(render=True, headless=False)