Quentin Gallouédec
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Browse files- .gitattributes +1 -0
- README.md +70 -0
- a2c-CartPole-v1.zip +3 -0
- a2c-CartPole-v1/_stable_baselines3_version +1 -0
- a2c-CartPole-v1/data +93 -0
- a2c-CartPole-v1/policy.optimizer.pth +3 -0
- a2c-CartPole-v1/policy.pth +3 -0
- a2c-CartPole-v1/pytorch_variables.pth +3 -0
- a2c-CartPole-v1/system_info.txt +7 -0
- args.yml +79 -0
- config.yml +9 -0
- env_kwargs.yml +1 -0
- replay.mp4 +3 -0
- results.json +1 -0
- train_eval_metrics.zip +3 -0
.gitattributes
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@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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library_name: stable-baselines3
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tags:
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- CartPole-v1
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name: A2C
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results:
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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: CartPole-v1
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type: CartPole-v1
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metrics:
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- type: mean_reward
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value: 500.00 +/- 0.00
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name: mean_reward
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verified: false
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---
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# **A2C** Agent playing **CartPole-v1**
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This is a trained model of a **A2C** agent playing **CartPole-v1**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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The RL Zoo is a training framework for Stable Baselines3
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reinforcement learning agents,
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with hyperparameter optimization and pre-trained agents included.
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## Usage (with SB3 RL Zoo)
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RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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Install the RL Zoo (with SB3 and SB3-Contrib):
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```bash
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pip install rl_zoo3
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```
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```
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# Download model and save it into the logs/ folder
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python -m rl_zoo3.load_from_hub --algo a2c --env CartPole-v1 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo a2c --env CartPole-v1 -f logs/
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```
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If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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```
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python -m rl_zoo3.load_from_hub --algo a2c --env CartPole-v1 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo a2c --env CartPole-v1 -f logs/
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```
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## Training (with the RL Zoo)
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```
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python -m rl_zoo3.train --algo a2c --env CartPole-v1 -f logs/
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# Upload the model and generate video (when possible)
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python -m rl_zoo3.push_to_hub --algo a2c --env CartPole-v1 -f logs/ -orga qgallouedec
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```
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## Hyperparameters
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```python
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OrderedDict([('ent_coef', 0.0),
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('n_envs', 8),
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('n_timesteps', 500000.0),
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('policy', 'MlpPolicy'),
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('normalize', False)])
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```
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a2c-CartPole-v1.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:abf599c147979859e98e8306fbfc543e51782cedd4e80b670509795ac1d25f1c
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size 97572
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a2c-CartPole-v1/_stable_baselines3_version
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1.8.0a6
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a2c-CartPole-v1/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
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"__module__": "stable_baselines3.common.policies",
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"__doc__": "\n 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\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: Features extractor to use.\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 ",
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"__init__": "<function ActorCriticPolicy.__init__ at 0x7f000d490d30>",
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f000d490dc0>",
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f000d490e50>",
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f000d490ee0>",
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"_build": "<function ActorCriticPolicy._build at 0x7f000d490f70>",
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"forward": "<function ActorCriticPolicy.forward at 0x7f000d493040>",
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"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f000d4930d0>",
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f000d493160>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7f000d4931f0>",
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f000d493280>",
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f000d493310>",
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f000d4933a0>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x7f000d492540>"
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},
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"verbose": 1,
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"policy_kwargs": {
|
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":type:": "<class 'dict'>",
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":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
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"optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
|
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"optimizer_kwargs": {
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"alpha": 0.99,
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"eps": 1e-05,
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+
"weight_decay": 0
|
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}
|
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},
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"observation_space": {
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":type:": "<class 'gym.spaces.box.Box'>",
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":serialized:": "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",
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"dtype": "float32",
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"_shape": [
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4
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],
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"low": "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]",
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"high": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]",
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"bounded_below": "[ True True True True]",
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"bounded_above": "[ True True True True]",
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"_np_random": null
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},
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"action_space": {
|
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":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
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ADDED
@@ -0,0 +1,79 @@
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1 |
+
!!python/object/apply:collections.OrderedDict
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2 |
+
- - - algo
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3 |
+
- a2c
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4 |
+
- - device
|
5 |
+
- auto
|
6 |
+
- - env
|
7 |
+
- CartPole-v1
|
8 |
+
- - env_kwargs
|
9 |
+
- null
|
10 |
+
- - eval_episodes
|
11 |
+
- 5
|
12 |
+
- - eval_freq
|
13 |
+
- 25000
|
14 |
+
- - gym_packages
|
15 |
+
- []
|
16 |
+
- - hyperparams
|
17 |
+
- null
|
18 |
+
- - log_folder
|
19 |
+
- logs
|
20 |
+
- - log_interval
|
21 |
+
- -1
|
22 |
+
- - max_total_trials
|
23 |
+
- null
|
24 |
+
- - n_eval_envs
|
25 |
+
- 1
|
26 |
+
- - n_evaluations
|
27 |
+
- null
|
28 |
+
- - n_jobs
|
29 |
+
- 1
|
30 |
+
- - n_startup_trials
|
31 |
+
- 10
|
32 |
+
- - n_timesteps
|
33 |
+
- -1
|
34 |
+
- - n_trials
|
35 |
+
- 500
|
36 |
+
- - no_optim_plots
|
37 |
+
- false
|
38 |
+
- - num_threads
|
39 |
+
- -1
|
40 |
+
- - optimization_log_path
|
41 |
+
- null
|
42 |
+
- - optimize_hyperparameters
|
43 |
+
- false
|
44 |
+
- - progress
|
45 |
+
- false
|
46 |
+
- - pruner
|
47 |
+
- median
|
48 |
+
- - sampler
|
49 |
+
- tpe
|
50 |
+
- - save_freq
|
51 |
+
- -1
|
52 |
+
- - save_replay_buffer
|
53 |
+
- false
|
54 |
+
- - seed
|
55 |
+
- 3872103898
|
56 |
+
- - storage
|
57 |
+
- null
|
58 |
+
- - study_name
|
59 |
+
- null
|
60 |
+
- - tensorboard_log
|
61 |
+
- runs/CartPole-v1__a2c__3872103898__1670922598
|
62 |
+
- - track
|
63 |
+
- true
|
64 |
+
- - trained_agent
|
65 |
+
- ''
|
66 |
+
- - truncate_last_trajectory
|
67 |
+
- true
|
68 |
+
- - uuid
|
69 |
+
- false
|
70 |
+
- - vec_env
|
71 |
+
- dummy
|
72 |
+
- - verbose
|
73 |
+
- 1
|
74 |
+
- - wandb_entity
|
75 |
+
- null
|
76 |
+
- - wandb_project_name
|
77 |
+
- quantitative-benchmark
|
78 |
+
- - yaml_file
|
79 |
+
- null
|
config.yml
ADDED
@@ -0,0 +1,9 @@
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|
|
1 |
+
!!python/object/apply:collections.OrderedDict
|
2 |
+
- - - ent_coef
|
3 |
+
- 0.0
|
4 |
+
- - n_envs
|
5 |
+
- 8
|
6 |
+
- - n_timesteps
|
7 |
+
- 500000.0
|
8 |
+
- - policy
|
9 |
+
- MlpPolicy
|
env_kwargs.yml
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{}
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replay.mp4
ADDED
@@ -0,0 +1,3 @@
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|
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|
|
|
|
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|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:dddfd7194c19e7196080c8f9eb8081e39544d511a9e8e1298da4f66202328016
|
3 |
+
size 54502
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results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 500.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-27T14:45:56.061042"}
|
train_eval_metrics.zip
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
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|
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|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:850e4190a9fdd1b63cb4f3a039d2f356bb48bf9caa468dfaece47968f2404ed0
|
3 |
+
size 38586
|