Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LiftCube-v0
     metrics:
     - type: mean_reward
-      value: 5.52 +/- 1.92
+      value: 5.06 +/- 4.05
       name: mean_reward
       verified: false
 ---
@@ -70,5 +70,5 @@ OrderedDict([('n_envs', 16),
 
 # Environment Arguments
 ```python
-{'render_mode': 'rgb_array'}
+{'observation_mode': 'state', 'render_mode': 'rgb_array'}
 ```

args.yml

@@ -8,7 +8,7 @@
   - - env
     - LiftCube-v0
   - - env_kwargs
-    - null
+    - observation_mode: state
   - - eval_env_kwargs
     - null
   - - eval_episodes
@@ -56,7 +56,7 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 68914943
+    - 3781158540
   - - storage
     - null
   - - study_name

env_kwargs.yml

@@ -1 +1,2 @@
+observation_mode: state
 render_mode: rgb_array

replay.mp4

@@ -1,3 +1,3 @@
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+size 94052

results.json

@@ -1 +1 @@
-{"mean_reward": 5.5155896, "std_reward": 1.9174329601274307, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-08T16:56:24.632879"}
+{"mean_reward": 5.0577847, "std_reward": 4.045849521693313, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-08T17:12:13.368558"}

tqc-LiftCube-v0.zip

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+size 3419894

tqc-LiftCube-v0/actor.optimizer.pth

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 size 590670

tqc-LiftCube-v0/critic.optimizer.pth

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 size 1255594

tqc-LiftCube-v0/data

@@ -4,20 +4,20 @@
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         "__module__": "sb3_contrib.tqc.policies",
         "__doc__": "\n    Policy class (with both actor and critic) for TQC.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n    :param features_extractor_class: Features extractor to use.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    :param n_quantiles: Number of quantiles for the critic.\n    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
-        "__init__": "<function MultiInputPolicy.__init__ at 0x7f1d96d0f370>",
+        "__init__": "<function MultiInputPolicy.__init__ at 0x7f4b161af370>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f1d96d1f100>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f4b161bf0c0>"
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     "verbose": 1,
     "policy_kwargs": {
         "use_sde": false
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     "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
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     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": null,
@@ -27,25 +27,25 @@
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     },
-    "_episode_num": 2369,
+    "_episode_num": 4992,
     "use_sde": false,
     "sde_sample_freq": -1,
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     "_stats_window_size": 100,
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