norm_reward=True
Browse files- README.md +37 -0
- a2c-PandaReachDense-v2.zip +3 -0
- a2c-PandaReachDense-v2/_stable_baselines3_version +1 -0
- a2c-PandaReachDense-v2/data +94 -0
- a2c-PandaReachDense-v2/policy.optimizer.pth +3 -0
- a2c-PandaReachDense-v2/policy.pth +3 -0
- a2c-PandaReachDense-v2/pytorch_variables.pth +3 -0
- a2c-PandaReachDense-v2/system_info.txt +7 -0
- config.json +1 -0
- replay.mp4 +0 -0
- results.json +1 -0
- vec_normalize.pkl +3 -0
README.md
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- PandaReachDense-v2
|
5 |
+
- deep-reinforcement-learning
|
6 |
+
- reinforcement-learning
|
7 |
+
- stable-baselines3
|
8 |
+
model-index:
|
9 |
+
- name: A2C
|
10 |
+
results:
|
11 |
+
- task:
|
12 |
+
type: reinforcement-learning
|
13 |
+
name: reinforcement-learning
|
14 |
+
dataset:
|
15 |
+
name: PandaReachDense-v2
|
16 |
+
type: PandaReachDense-v2
|
17 |
+
metrics:
|
18 |
+
- type: mean_reward
|
19 |
+
value: -2.10 +/- 0.98
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **A2C** Agent playing **PandaReachDense-v2**
|
25 |
+
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
|
26 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
27 |
+
|
28 |
+
## Usage (with Stable-baselines3)
|
29 |
+
TODO: Add your code
|
30 |
+
|
31 |
+
|
32 |
+
```python
|
33 |
+
from stable_baselines3 import ...
|
34 |
+
from huggingface_sb3 import load_from_hub
|
35 |
+
|
36 |
+
...
|
37 |
+
```
|
a2c-PandaReachDense-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e1e5e7d5c86dc5ecefcc44ffdfdf60fd2b40363d820bb2adc7259138df9c158c
|
3 |
+
size 108023
|
a2c-PandaReachDense-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.7.0
|
a2c-PandaReachDense-v2/data
ADDED
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"policy_class": {
|
3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
|
5 |
+
"__module__": "stable_baselines3.common.policies",
|
6 |
+
"__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 ",
|
7 |
+
"__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7ff579df6e50>",
|
8 |
+
"__abstractmethods__": "frozenset()",
|
9 |
+
"_abc_impl": "<_abc_data object at 0x7ff579df9120>"
|
10 |
+
},
|
11 |
+
"verbose": 1,
|
12 |
+
"policy_kwargs": {
|
13 |
+
":type:": "<class 'dict'>",
|
14 |
+
":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
|
15 |
+
"optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
|
16 |
+
"optimizer_kwargs": {
|
17 |
+
"alpha": 0.99,
|
18 |
+
"eps": 1e-05,
|
19 |
+
"weight_decay": 0
|
20 |
+
}
|
21 |
+
},
|
22 |
+
"observation_space": {
|
23 |
+
":type:": "<class 'gym.spaces.dict.Dict'>",
|
24 |
+
":serialized:": "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",
|
25 |
+
"spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
|
26 |
+
"_shape": null,
|
27 |
+
"dtype": null,
|
28 |
+
"_np_random": null
|
29 |
+
},
|
30 |
+
"action_space": {
|
31 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
32 |
+
":serialized:": "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",
|
33 |
+
"dtype": "float32",
|
34 |
+
"_shape": [
|
35 |
+
3
|
36 |
+
],
|
37 |
+
"low": "[-1. -1. -1.]",
|
38 |
+
"high": "[1. 1. 1.]",
|
39 |
+
"bounded_below": "[ True True True]",
|
40 |
+
"bounded_above": "[ True True True]",
|
41 |
+
"_np_random": null
|
42 |
+
},
|
43 |
+
"n_envs": 4,
|
44 |
+
"num_timesteps": 1000000,
|
45 |
+
"_total_timesteps": 1000000,
|
46 |
+
"_num_timesteps_at_start": 0,
|
47 |
+
"seed": null,
|
48 |
+
"action_noise": null,
|
49 |
+
"start_time": 1676973811900151569,
|
50 |
+
"learning_rate": 0.0007,
|
51 |
+
"tensorboard_log": null,
|
52 |
+
"lr_schedule": {
|
53 |
+
":type:": "<class 'function'>",
|
54 |
+
":serialized:": "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"
|
55 |
+
},
|
56 |
+
"_last_obs": {
|
57 |
+
":type:": "<class 'collections.OrderedDict'>",
|
58 |
+
":serialized:": "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",
|
59 |
+
"achieved_goal": "[[ 0.3944578 -0.0157414 0.56241876]\n [ 0.3944578 -0.0157414 0.56241876]\n [ 0.3944578 -0.0157414 0.56241876]\n [ 0.3944578 -0.0157414 0.56241876]]",
|
60 |
+
"desired_goal": "[[-1.3788594 -0.6513883 -0.8619297 ]\n [ 0.56318676 1.1726551 1.1085063 ]\n [ 1.1985343 0.06257951 0.35181746]\n [-1.5395296 0.2535844 0.97488016]]",
|
61 |
+
"observation": "[[ 0.3944578 -0.0157414 0.56241876 0.0108197 -0.00137591 0.0090893 ]\n [ 0.3944578 -0.0157414 0.56241876 0.0108197 -0.00137591 0.0090893 ]\n [ 0.3944578 -0.0157414 0.56241876 0.0108197 -0.00137591 0.0090893 ]\n [ 0.3944578 -0.0157414 0.56241876 0.0108197 -0.00137591 0.0090893 ]]"
|
62 |
+
},
|
63 |
+
"_last_episode_starts": {
|
64 |
+
":type:": "<class 'numpy.ndarray'>",
|
65 |
+
":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
|
66 |
+
},
|
67 |
+
"_last_original_obs": {
|
68 |
+
":type:": "<class 'collections.OrderedDict'>",
|
69 |
+
":serialized:": "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",
|
70 |
+
"achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]",
|
71 |
+
"desired_goal": "[[-0.03391175 -0.01134746 0.25515994]\n [-0.01467543 -0.0993484 0.04216356]\n [-0.00795777 0.09611931 0.08271492]\n [ 0.12598887 -0.00106764 0.02519052]]",
|
72 |
+
"observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"
|
73 |
+
},
|
74 |
+
"_episode_num": 0,
|
75 |
+
"use_sde": false,
|
76 |
+
"sde_sample_freq": -1,
|
77 |
+
"_current_progress_remaining": 0.0,
|
78 |
+
"ep_info_buffer": {
|
79 |
+
":type:": "<class 'collections.deque'>",
|
80 |
+
":serialized:": "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"
|
81 |
+
},
|
82 |
+
"ep_success_buffer": {
|
83 |
+
":type:": "<class 'collections.deque'>",
|
84 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
85 |
+
},
|
86 |
+
"_n_updates": 50000,
|
87 |
+
"n_steps": 5,
|
88 |
+
"gamma": 0.99,
|
89 |
+
"gae_lambda": 1.0,
|
90 |
+
"ent_coef": 0.0,
|
91 |
+
"vf_coef": 0.5,
|
92 |
+
"max_grad_norm": 0.5,
|
93 |
+
"normalize_advantage": false
|
94 |
+
}
|
a2c-PandaReachDense-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:814c52a304837a90eafce36ce05654f358ade7aef3b36a21cc446ecfcd9a4537
|
3 |
+
size 44734
|
a2c-PandaReachDense-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d4a4604dc27bff99bbd818dca1c6706b1efb316d61df86da20bdeb44b388d2a9
|
3 |
+
size 46014
|
a2c-PandaReachDense-v2/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
3 |
+
size 431
|
a2c-PandaReachDense-v2/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
|
2 |
+
- Python: 3.8.10
|
3 |
+
- Stable-Baselines3: 1.7.0
|
4 |
+
- PyTorch: 1.13.1+cu116
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.21.6
|
7 |
+
- Gym: 0.21.0
|
config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7ff579df6e50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff579df9120>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676973811900151569, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.3944578 -0.0157414 0.56241876]\n [ 0.3944578 -0.0157414 0.56241876]\n [ 0.3944578 -0.0157414 0.56241876]\n [ 0.3944578 -0.0157414 0.56241876]]", "desired_goal": "[[-1.3788594 -0.6513883 -0.8619297 ]\n [ 0.56318676 1.1726551 1.1085063 ]\n [ 1.1985343 0.06257951 0.35181746]\n [-1.5395296 0.2535844 0.97488016]]", "observation": "[[ 0.3944578 -0.0157414 0.56241876 0.0108197 -0.00137591 0.0090893 ]\n [ 0.3944578 -0.0157414 0.56241876 0.0108197 -0.00137591 0.0090893 ]\n [ 0.3944578 -0.0157414 0.56241876 0.0108197 -0.00137591 0.0090893 ]\n [ 0.3944578 -0.0157414 0.56241876 0.0108197 -0.00137591 0.0090893 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]", "desired_goal": "[[-0.03391175 -0.01134746 0.25515994]\n [-0.01467543 -0.0993484 0.04216356]\n [-0.00795777 0.09611931 0.08271492]\n [ 0.12598887 -0.00106764 0.02519052]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
replay.mp4
ADDED
Binary file (323 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": -2.1047943426296114, "std_reward": 0.9827160596909064, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-21T10:53:58.230011"}
|
vec_normalize.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:093ead10a2fbe42fd50b53e8eb9054d170b638622cf6f29b54c25b800aabe576
|
3 |
+
size 3056
|