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+    "ankle_angle": 204,
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+    "mtp_angle": 206,
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+    "knee_angle_beta_rotation1": 209,
+    "foot": 210,
+    "toes": 211,
+    "root": 212,
+    "contact": 213
+}

baoding_phase_1/CustomMyoBaodingBallsP1_config.json

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+{
+    "weighted_reward_keys": {
+        "pos_dist_1": 1,
+        "pos_dist_2": 1,
+        "act_reg": 0,
+        "alive": 1,
+        "solved": 5,
+        "done": 0,
+        "sparse": 0
+    },
+    "task": "random",
+    "enable_rsi": false,
+    "rsi_probability": 0,
+    "noise_palm": 0,
+    "noise_fingers": 0,
+    "noise_balls": 0,
+    "goal_time_period": [
+        5,
+        5
+    ],
+    "goal_xrange": [
+        0.025,
+        0.025
+    ],
+    "goal_yrange": [
+        0.028,
+        0.028
+    ],
+    "drop_th": 1.3
+}

baoding_phase_1/RecurrentPPO_1/events.out.tfevents.1666173178.AMG19.3640388.0

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+size 2544462

baoding_phase_1/env.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:73774dc8c4ccd4bf1e99439c0e86bdee1446ab641c771429756e5063c05f255c
+size 20038

baoding_phase_1/evaluations.npz

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+version https://git-lfs.github.com/spec/v1
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+size 55874

baoding_phase_1/main.py

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+import copy
+import json
+import os
+import shutil
+from calendar import c
+from datetime import datetime
+
+import numpy as np
+import torch.nn as nn
+from sb3_contrib import RecurrentPPO
+from stable_baselines3.common.callbacks import EvalCallback
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.vec_env import VecNormalize
+from stable_baselines3.common.vec_env.subproc_vec_env import SubprocVecEnv
+
+from src.envs.environment_factory import EnvironmentFactory
+from src.metrics.custom_callbacks import EvaluateLSTM
+from src.metrics.sb_callbacks import EnvDumpCallback
+
+env_name = "CustomMyoBaodingBallsP1"
+
+# saving criteria
+saving_criteria = "dense_rewards" # dense_rewards, score
+
+# whether this is the first task of the curriculum (True) or it is loading a previous task (False)
+FIRST_TASK = False
+
+# Path to normalized Vectorized environment (if not first task)
+PATH_TO_NORMALIZED_ENV = "trained_models/random/08-28-26_random_6_6/training_env.pkl"
+
+# Path to pretrained network (if not first task)
+PATH_TO_PRETRAINED_NET = "trained_models/random/08-28-26_random_6_6/best_model.zip"
+
+# Tensorboard log (will save best model during evaluation)
+now = datetime.now().strftime("%Y-%m-%d/%H-%M-%S") + "_random_5_5"
+TENSORBOARD_LOG = os.path.join("output", "training", now)
+
+
+# Reward structure and task parameters:
+config = {
+    "weighted_reward_keys": {
+        "pos_dist_1": 1,
+        "pos_dist_2": 1,
+        "act_reg": 0,
+        "alive": 1,
+        "solved": 5,
+        "done": 0,
+        "sparse": 0,
+    },
+    "task": "random",
+    "enable_rsi": False,
+    "rsi_probability": 0,
+    "noise_palm": 0,
+    "noise_fingers": 0,
+    "noise_balls": 0,
+    "goal_time_period": [5, 5],   # phase 2: (4, 6)
+    "goal_xrange": (0.025, 0.025),  # phase 2: (0.020, 0.030)
+    "goal_yrange": (0.028, 0.028),  # phase 2: (0.022, 0.032)
+    "drop_th": 1.3,
+}
+
+# Function that creates and monitors vectorized environments:
+def make_parallel_envs(env_name, env_config, num_env, start_index=0):
+    def make_env(rank):
+        def _thunk():
+            env = EnvironmentFactory.register(env_name, **env_config)
+            env = Monitor(env, TENSORBOARD_LOG)
+            return env
+
+        return _thunk
+
+    return SubprocVecEnv([make_env(i + start_index) for i in range(num_env)])
+
+
+if __name__ == "__main__":
+    os.makedirs(TENSORBOARD_LOG, exist_ok=True)
+    with open(os.path.join(TENSORBOARD_LOG, "config.json"), "w") as file:
+        json.dump(config, file)
+    shutil.copy(os.path.abspath(__file__), TENSORBOARD_LOG)
+
+    # Create vectorized environments:
+    envs = make_parallel_envs(env_name, config, num_env=16)
+
+    # Normalize environment:
+    if FIRST_TASK:
+        envs = VecNormalize(envs)
+    else:
+        envs = VecNormalize.load(PATH_TO_NORMALIZED_ENV, envs)
+
+
+    # Callbacks for score and for effort
+
+    config_score, config_effort = copy.deepcopy(config), copy.deepcopy(config)
+
+    config_score['weighted_reward_keys'].update({
+        'pos_dist_1': 0,
+        'pos_dist_2': 0,
+        'act_reg': 0,
+        'solved': 5,
+        'alive':0,
+        'done': 0,
+        'sparse': 0})
+
+    config_effort['weighted_reward_keys'].update({
+        'pos_dist_1': 0,
+        'pos_dist_2': 0,
+        'act_reg': 1,
+        'solved': 0,
+        'alive':0,
+        'done': 0,
+        'sparse': 0})
+
+    env_score = EnvironmentFactory.register(env_name, **config_score)
+    env_effort = EnvironmentFactory.register(env_name, **config_effort)
+
+    score_callback = EvaluateLSTM(eval_freq = 5000, eval_env = env_score, name = 'eval/score', num_episodes=10)
+    effort_callback = EvaluateLSTM(eval_freq = 5000, eval_env = env_effort, name = 'eval/effort', num_episodes=10)
+
+    # Evaluation Callback
+
+    # Create vectorized environments:
+    if saving_criteria=="score":
+        eval_envs = make_parallel_envs(env_name, config_score, num_env=16)
+    elif saving_criteria=="dense_rewards":
+        eval_envs = make_parallel_envs(env_name, config, num_env=16)
+    else:
+        raise ValueError('Unrecognized saving criteria')
+
+    if FIRST_TASK:
+        eval_envs = VecNormalize(eval_envs)
+    else:
+        eval_envs = VecNormalize.load(PATH_TO_NORMALIZED_ENV, eval_envs) 
+
+    env_dump_callback = EnvDumpCallback(TENSORBOARD_LOG, verbose=0)
+
+    eval_callback = EvalCallback(
+        eval_envs,
+        callback_on_new_best=env_dump_callback,
+        best_model_save_path=TENSORBOARD_LOG,
+        log_path=TENSORBOARD_LOG,
+        eval_freq=2500,
+        deterministic=True,
+        render=False,
+        n_eval_episodes=20,
+    )
+
+    # Create model (hyperparameters from RL Zoo HalfCheetak)
+    if FIRST_TASK:
+        model = RecurrentPPO(
+            "MlpLstmPolicy",
+            envs,
+            verbose=2,
+            tensorboard_log=TENSORBOARD_LOG,
+            batch_size=32,
+            n_steps=512,
+            gamma=0.99,
+            gae_lambda=0.9,
+            n_epochs=10,
+            ent_coef= 3e-6,
+            learning_rate=2e-5,
+            clip_range=0.25,
+            use_sde=True,
+            max_grad_norm=0.8,
+            vf_coef=0.5,
+            policy_kwargs=dict(
+                log_std_init=-2,
+                ortho_init=False,
+                activation_fn=nn.ReLU,
+                net_arch=[dict(pi=[], vf=[])],
+                enable_critic_lstm=True,
+                lstm_hidden_size=128,
+            ),
+        )
+    else:
+        model = RecurrentPPO.load(
+            PATH_TO_PRETRAINED_NET, env=envs, tensorboard_log=TENSORBOARD_LOG, device='cuda'
+        )
+
+    # Train and save model
+    model.learn(
+        total_timesteps=10_000_000, callback=[eval_callback,score_callback,effort_callback], reset_num_timesteps=True
+    )
+
+    model.save(os.path.join(TENSORBOARD_LOG, "final_model.pkl"))
+    envs.save(os.path.join(TENSORBOARD_LOG, "final_env.pkl"))

baoding_phase_1/model.zip

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baoding_phase_2/RecurrentPPO_1/events.out.tfevents.1667462073.AMG19.4003430.0

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+oid sha256:f1e822a3d1433191800d5c38c86b182dee81f3fb6447541dd6fa026581b21f25
+size 233218

baoding_phase_2/config.json

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+{
+    "weighted_reward_keys": {
+        "pos_dist_1": 5,
+        "pos_dist_2": 5,
+        "act_reg": 0,
+        "alive": 1,
+        "solved": 5,
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+    "enable_rsi": false,
+    "rsi_probability": 0,
+    "balls_overlap": false,
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+    "noise_fingers": 0,
+    "limit_init_angle": 3.141592653589793,
+    "goal_time_period": [
+        4,
+        6
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+    "goal_xrange": [
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+    "task_choice": "random"
+}

baoding_phase_2/env.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d389ce40339179b9e44fd127b9c486fcafd0bd17ec6c70feec5da924b0c0b223
+size 20038

baoding_phase_2/main.py

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+import copy
+import json
+import os
+import shutil
+from datetime import datetime
+
+import numpy as np
+import torch.nn as nn
+from sb3_contrib import RecurrentPPO
+from stable_baselines3.common.callbacks import EvalCallback, CheckpointCallback
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.vec_env import VecNormalize
+from stable_baselines3.common.vec_env.subproc_vec_env import SubprocVecEnv
+
+from src.envs.environment_factory import EnvironmentFactory
+from src.metrics.custom_callbacks import EvaluateLSTM
+from src.metrics.sb_callbacks import EnvDumpCallback
+
+env_name = "CustomMyoBaodingBallsP2"
+
+# saving criteria
+saving_criteria = "dense_rewards"  # score
+
+# whether this is the first task of the curriculum (True) or it is loading a previous task (False)
+FIRST_TASK = False
+
+# # Path to normalized Vectorized environment and best model (if not first task)
+# load_folder = "trained_models/baoding_phase2/00-45-16_final_nisheet_score-36"
+# PATH_TO_NORMALIZED_ENV = load_folder + "/training_env.pkl"
+# PATH_TO_PRETRAINED_NET = load_folder + "/best_model.zip"
+# Path to normalized Vectorized environment (if not first task)
+PATH_TO_NORMALIZED_ENV = "output/training/2022-11-02/10-47-05_nisheet_36_new_hp_pos_dist_5_alive_1_solved_5/final_env.pkl"  # "trained_models/normalized_env_original"
+
+# Path to pretrained network (if not first task)
+PATH_TO_PRETRAINED_NET = "output/training/2022-11-02/10-47-05_nisheet_36_new_hp_pos_dist_5_alive_1_solved_5/final_model.pkl"  # "trained_models/best_model.zip"
+
+# Tensorboard log (will save best model during evaluation)
+now = (
+    datetime.now().strftime("%Y-%m-%d/%H-%M-%S")
+    + "_nisheet_36_new_hp_pos_dist_5_alive_1_solved_5_resume"
+)
+TENSORBOARD_LOG = os.path.join("output", "training", now)
+
+
+# Reward structure and task parameters:
+config = {
+    "weighted_reward_keys": {
+        "pos_dist_1": 5,
+        "pos_dist_2": 5,
+        "act_reg": 0,
+        "alive": 1,
+        "solved": 5,
+        "done": 0,
+        "sparse": 0,
+    },
+    # custom params for curriculum learning
+    "enable_rsi": False,
+    "rsi_probability": 0,
+    "balls_overlap": False,
+    "overlap_probability": 0,
+    "noise_fingers": 0,
+    "limit_init_angle": np.pi,
+    # "beta_init_angle": [1.5,1.5],   # caution: doesn't work if limit_init_angle = False
+    "goal_time_period": [4, 6],  # phase 2: (4, 6)
+    "goal_xrange": (0.020, 0.030),  # phase 2: (0.020, 0.030)
+    "goal_yrange": (0.022, 0.032),  # phase 2: (0.022, 0.032)
+    # Randomization in physical properties of the baoding balls
+    "obj_size_range": (
+        0.018,
+        0.024,
+    ),  # (0.018, 0.024   # Object size range. Nominal 0.022
+    # "beta_ball_size": [1.5,1.5],
+    "obj_mass_range": (
+        0.030,
+        0.300,
+    ),  # (0.030, 0.300)   # Object weight range. Nominal 43 gms
+    # "beta_ball_mass": [1.5,1.5],
+    "obj_friction_change": (0.2, 0.001, 0.00002),  # (0.2, 0.001, 0.00002)
+    # Task
+    "task_choice": "random",
+}
+
+# Function that creates and monitors vectorized environments:
+def make_parallel_envs(
+    env_name, env_config, num_env, start_index=0
+):  # pylint: disable=redefined-outer-name
+    def make_env(_):
+        def _thunk():
+            env = EnvironmentFactory.register(env_name, **env_config)
+            env = Monitor(env, TENSORBOARD_LOG)
+            return env
+
+        return _thunk
+
+    return SubprocVecEnv([make_env(i + start_index) for i in range(num_env)])
+
+
+if __name__ == "__main__":
+    os.makedirs(TENSORBOARD_LOG, exist_ok=True)
+    with open(
+        os.path.join(TENSORBOARD_LOG, "config.json"), "w"
+    ) as file:  # pylint: disable=unspecified-encoding
+        json.dump(config, file)
+    shutil.copy(os.path.abspath(__file__), TENSORBOARD_LOG)
+
+    # Create vectorized environments:
+    envs = make_parallel_envs(env_name, config, num_env=16)
+
+    # Normalize environment:
+    if FIRST_TASK:
+        envs = VecNormalize(envs)
+    else:
+        envs = VecNormalize.load(PATH_TO_NORMALIZED_ENV, envs)
+
+    # Callbacks for score and for effort
+    config_score, config_effort = copy.deepcopy(config), copy.deepcopy(config)
+
+    config_score.update(
+        {
+            "weighted_reward_keys": {
+                "pos_dist_1": 0,
+                "pos_dist_2": 0,
+                "act_reg": 0,
+                "solved": 5,
+                "alive": 0,
+                "done": 0,
+                "sparse": 0,
+            },
+            # score on the final noise distribution
+            "noise_fingers": 0,
+            "limit_init_angle": False,
+            "beta_init_angle": False,
+            "beta_ball_size": False,
+            "beta_ball_mass": False,
+        }
+    )
+
+    config_effort.update(
+        {
+            "weighted_reward_keys": {
+                "pos_dist_1": 0,
+                "pos_dist_2": 0,
+                "act_reg": 1,
+                "solved": 0,
+                "alive": 0,
+                "done": 0,
+                "sparse": 0,
+            },
+            # effort on the final noise distribution
+            "noise_fingers": 0,
+            "limit_init_angle": False,
+            "beta_init_angle": False,
+            "beta_ball_size": False,
+            "beta_ball_mass": False,
+        }
+    )
+
+    env_score = EnvironmentFactory.create(env_name, **config_score)
+    env_effort = EnvironmentFactory.create(env_name, **config_effort)
+
+    score_callback = EvaluateLSTM(
+        eval_freq=50000, eval_env=env_score, name="eval/score", num_episodes=20
+    )
+    effort_callback = EvaluateLSTM(
+        eval_freq=5000, eval_env=env_effort, name="eval/effort", num_episodes=1
+    )
+
+    # Evaluation Callback
+
+    # Create vectorized environments:
+    if saving_criteria == "score":
+        eval_envs = make_parallel_envs(env_name, config_score, num_env=1)
+    elif saving_criteria == "dense_rewards":
+        eval_envs = make_parallel_envs(env_name, config, num_env=1)
+    else:
+        raise ValueError("Unrecognized saving criteria")
+
+    if FIRST_TASK:
+        eval_envs = VecNormalize(eval_envs)
+    else:
+        eval_envs = VecNormalize.load(PATH_TO_NORMALIZED_ENV, eval_envs)
+
+    env_dump_callback = EnvDumpCallback(TENSORBOARD_LOG, verbose=0)
+
+    eval_callback = EvalCallback(
+        eval_envs,
+        callback_on_new_best=env_dump_callback,
+        best_model_save_path=TENSORBOARD_LOG,
+        log_path=TENSORBOARD_LOG,
+        eval_freq=2_500,
+        deterministic=True,
+        render=False,
+        n_eval_episodes=20,
+    )
+
+    checkpoint_callback = CheckpointCallback(
+        save_freq=10000, save_path=TENSORBOARD_LOG, save_vecnormalize=True
+    )
+
+    # Create model (hyperparameters from RL Zoo HalfCheetak)
+    if FIRST_TASK:
+        model = RecurrentPPO(
+            "MlpLstmPolicy",
+            envs,
+            verbose=2,
+            tensorboard_log=TENSORBOARD_LOG,
+            batch_size=32,
+            n_steps=512,
+            gamma=0.99,
+            gae_lambda=0.9,
+            n_epochs=10,
+            ent_coef=3e-6,
+            learning_rate=2e-5,
+            clip_range=0.25,
+            use_sde=True,
+            max_grad_norm=0.8,
+            vf_coef=0.5,
+            policy_kwargs=dict(
+                log_std_init=-2,
+                ortho_init=False,
+                activation_fn=nn.ReLU,
+                net_arch=[dict(pi=[], vf=[])],
+                enable_critic_lstm=True,
+                lstm_hidden_size=128,
+            ),
+        )
+    else:
+        custom_objects = {
+            "lr_schedule": lambda _: 5e-05,
+            "learning_rate": lambda _: 5e-05,
+            "clip_range": 0.3,
+            "n_steps": 4096,
+            "batch_size": 4096,
+            "ent_coef": 0.00025,
+            "vf_coef": 1,
+        }
+        model = RecurrentPPO.load(
+            PATH_TO_PRETRAINED_NET,
+            env=envs,
+            tensorboard_log=TENSORBOARD_LOG,
+            device="cuda",
+            custom_objects=custom_objects,
+        )
+
+    # Train and save model
+    model.learn(
+        total_timesteps=50_000_000,
+        # callback=[eval_callback, score_callback, effort_callback, checkpoint_callback],
+        callback=[score_callback, checkpoint_callback],
+        reset_num_timesteps=True,
+    )
+
+    model.save(os.path.join(TENSORBOARD_LOG, "final_model.pkl"))
+    envs.save(os.path.join(TENSORBOARD_LOG, "final_env.pkl"))

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