Upload PPO LunarLander-v2 trained agent
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +94 -0
- ppo-LunarLander-v2/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2/policy.pth +3 -0
- ppo-LunarLander-v2/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2/system_info.txt +7 -0
- replay.mp4 +0 -0
- results.json +1 -0
README.md
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
library_name: stable-baselines3
|
| 3 |
+
tags:
|
| 4 |
+
- LunarLander-v2
|
| 5 |
+
- deep-reinforcement-learning
|
| 6 |
+
- reinforcement-learning
|
| 7 |
+
- stable-baselines3
|
| 8 |
+
model-index:
|
| 9 |
+
- name: PPO
|
| 10 |
+
results:
|
| 11 |
+
- task:
|
| 12 |
+
type: reinforcement-learning
|
| 13 |
+
name: reinforcement-learning
|
| 14 |
+
dataset:
|
| 15 |
+
name: LunarLander-v2
|
| 16 |
+
type: LunarLander-v2
|
| 17 |
+
metrics:
|
| 18 |
+
- type: mean_reward
|
| 19 |
+
value: 235.07 +/- 44.80
|
| 20 |
+
name: mean_reward
|
| 21 |
+
verified: false
|
| 22 |
+
---
|
| 23 |
+
|
| 24 |
+
# **PPO** Agent playing **LunarLander-v2**
|
| 25 |
+
This is a trained model of a **PPO** agent playing **LunarLander-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 |
+
```
|
config.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 ActorCriticPolicy.__init__ at 0x7f8ae5a3b3a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8ae5a3b430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8ae5a3b4c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8ae5a3b550>", "_build": "<function ActorCriticPolicy._build at 0x7f8ae5a3b5e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f8ae5a3b670>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8ae5a3b700>", "_predict": "<function ActorCriticPolicy._predict at 0x7f8ae5a3b790>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8ae5a3b820>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8ae5a3b8b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8ae5a3b940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f8ae5a30fc0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672248108033923646, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVeRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIke9S6lLhcUCUhpRSlIwBbJRNkgGMAXSUR0CQlVq8DjiodX2UKGgGaAloD0MIYOY7+ImlcUCUhpRSlGgVTW0BaBZHQJCWYqnWJ791fZQoaAZoCWgPQwhuh4bFqEZwQJSGlFKUaBVNDgJoFkdAkJZ6//NqxnV9lChoBmgJaA9DCHDqA8m7EXBAlIaUUpRoFU10AWgWR0CQlrnSv1UVdX2UKGgGaAloD0MI+G2I8RolbkCUhpRSlGgVTZsBaBZHQJCsHpNbkfd1fZQoaAZoCWgPQwimRuhn6sJxQJSGlFKUaBVNPAFoFkdAkKx1tbcGknV9lChoBmgJaA9DCOT1YFJ8f21AlIaUUpRoFU1MAWgWR0CQrHbfP5YYdX2UKGgGaAloD0MIo7CLogcCckCUhpRSlGgVTZMBaBZHQJCsk3bVSXN1fZQoaAZoCWgPQwjji/Z4YdVwQJSGlFKUaBVNcAFoFkdAkK5iAH3UQXV9lChoBmgJaA9DCK6BrRIsuExAlIaUUpRoFU0KAWgWR0CQrx/Ot4iYdX2UKGgGaAloD0MIUMQihl3YcUCUhpRSlGgVTU0BaBZHQJCvp6PbO/t1fZQoaAZoCWgPQwhoeR7cnYdyQJSGlFKUaBVNUQFoFkdAkK+4YJmdy3V9lChoBmgJaA9DCHujVph+4nBAlIaUUpRoFU1CAWgWR0CQsLghr30xdX2UKGgGaAloD0MI0hqDTgifRkCUhpRSlGgVS+5oFkdAkLDC1NQCS3V9lChoBmgJaA9DCFzLZDie13FAlIaUUpRoFU2WAWgWR0CQsPNlRP43dX2UKGgGaAloD0MIhEcbR+xxcECUhpRSlGgVTUoBaBZHQJCxObsniNt1fZQoaAZoCWgPQwjyQc9m1RcnQJSGlFKUaBVNEQFoFkdAkLGXTd+G5HV9lChoBmgJaA9DCC/5n/xdempAlIaUUpRoFU06AWgWR0CQsqbMHKOldX2UKGgGaAloD0MIg9pv7YSncECUhpRSlGgVTXgBaBZHQJCzk1P3ztl1fZQoaAZoCWgPQwhBYVCmUSZuQJSGlFKUaBVNlgFoFkdAkLRvIsAeaXV9lChoBmgJaA9DCE+sU+V7u2xAlIaUUpRoFU1VAWgWR0CQt1KGtZFHdX2UKGgGaAloD0MIGcVyS+vBcUCUhpRSlGgVTSgBaBZHQJC4BmXgLql1fZQoaAZoCWgPQwhj00ohkLZrQJSGlFKUaBVNdAFoFkdAkLgy6tknTnV9lChoBmgJaA9DCHSZmgTvV3BAlIaUUpRoFU1rAWgWR0CQuEM23rledX2UKGgGaAloD0MIwAMDCB/QbkCUhpRSlGgVTXABaBZHQJC4keKbayt1fZQoaAZoCWgPQwjAWyBBcbFxQJSGlFKUaBVNJQFoFkdAkLix8twrD3V9lChoBmgJaA9DCMobYOY7TD1AlIaUUpRoFU0bAWgWR0CQunyBTXJ6dX2UKGgGaAloD0MIlgUTf5SycECUhpRSlGgVTUABaBZHQJC7Uu+RHPN1fZQoaAZoCWgPQwjiWu1hr6xxQJSGlFKUaBVNWwFoFkdAkLyAntv4unV9lChoBmgJaA9DCAVvSKOClG1AlIaUUpRoFU1iAWgWR0CQvIwg1WKedX2UKGgGaAloD0MIQ46tZ4h5bECUhpRSlGgVTYABaBZHQJC8hwxWT5h1fZQoaAZoCWgPQwjvObAc4chwQJSGlFKUaBVNJgFoFkdAkL2lvAGjbnV9lChoBmgJaA9DCBu8r8qFtm1AlIaUUpRoFU1PAWgWR0CQvgaM72csdX2UKGgGaAloD0MIuYrFbwoSbECUhpRSlGgVTUcBaBZHQJC/pr1uivh1fZQoaAZoCWgPQwj59NiWAT8vQJSGlFKUaBVL92gWR0CQwGn27FsIdX2UKGgGaAloD0MIJjj1geQoUECUhpRSlGgVS/BoFkdAkMCmznied3V9lChoBmgJaA9DCMNjP4ulT3FAlIaUUpRoFU3YAWgWR0CQwTBIFvAHdX2UKGgGaAloD0MIcvp6vma7cECUhpRSlGgVTRUBaBZHQJDBjVurIYF1fZQoaAZoCWgPQwgIdCZtKqhvQJSGlFKUaBVNIwFoFkdAkMIFOwgTy3V9lChoBmgJaA9DCIS7s3Zb6WxAlIaUUpRoFU1UAWgWR0CQwuFGXokidX2UKGgGaAloD0MI/tKiPsmlcECUhpRSlGgVTWABaBZHQJDEa04R28t1fZQoaAZoCWgPQwjxuKgWEcXeP5SGlFKUaBVNCAFoFkdAkMUH5WRzR3V9lChoBmgJaA9DCFFPH4F/BXBAlIaUUpRoFU0xAWgWR0CQxVz6rNnodX2UKGgGaAloD0MICrq9pDEyQUCUhpRSlGgVS+BoFkdAkMcfT5O8CnV9lChoBmgJaA9DCCaL+49MhW5AlIaUUpRoFU1NAWgWR0CQx3ncL0BfdX2UKGgGaAloD0MIP1WFBuLCbkCUhpRSlGgVTVoBaBZHQJDH+fWcz691fZQoaAZoCWgPQwi0rWad8XNxQJSGlFKUaBVNmwFoFkdAkMhKHoHLR3V9lChoBmgJaA9DCJuvko9dBXJAlIaUUpRoFU06AWgWR0CQyHfIjnmrdX2UKGgGaAloD0MIUp55OWw5a0CUhpRSlGgVTUUBaBZHQJDIeObRWtF1fZQoaAZoCWgPQwh90LNZdYltQJSGlFKUaBVNRAFoFkdAkMzbFS88LnV9lChoBmgJaA9DCDbJj/iVrG9AlIaUUpRoFU09AWgWR0CQzT4Z/CqIdX2UKGgGaAloD0MIGQPrOH7QcECUhpRSlGgVTXEBaBZHQJDNqJ/G2kV1fZQoaAZoCWgPQwhDN/sDZSdwQJSGlFKUaBVNPgFoFkdAkODFUEPlMnV9lChoBmgJaA9DCLmI78SsZ3FAlIaUUpRoFU2VAWgWR0CQ4SsY2sJZdX2UKGgGaAloD0MItHHEWvx6cECUhpRSlGgVTX8BaBZHQJDhNcgQpWp1fZQoaAZoCWgPQwjECUyndQBuQJSGlFKUaBVNRwFoFkdAkOPIznA6+3V9lChoBmgJaA9DCGpq2VoflnBAlIaUUpRoFU0aAWgWR0CQ4/1dgOSXdX2UKGgGaAloD0MIJHuEmiF7QUCUhpRSlGgVS/toFkdAkOQNqDbrT3V9lChoBmgJaA9DCFtB0xIrOV1AlIaUUpRoFU3oA2gWR0CQ5G1s+FDfdX2UKGgGaAloD0MIq+l6ouuGQECUhpRSlGgVTQQBaBZHQJDkhYZEUj91fZQoaAZoCWgPQwi9j6M5MuZxQJSGlFKUaBVNgAFoFkdAkOVfMW43FXV9lChoBmgJaA9DCPYn8blTBHFAlIaUUpRoFU2XAWgWR0CQ5YnBtUGWdX2UKGgGaAloD0MIn1bRH9qKckCUhpRSlGgVTUoBaBZHQJDmONCJGfB1fZQoaAZoCWgPQwhIUz2Zf19vQJSGlFKUaBVNVwFoFkdAkOb5QDV6NXV9lChoBmgJaA9DCGsLz0tFpXBAlIaUUpRoFU0rAWgWR0CQ6hrfLs8gdX2UKGgGaAloD0MINZawNkZwcUCUhpRSlGgVTUUBaBZHQJDqZiay8jB1fZQoaAZoCWgPQwjnq+Rj9xxxQJSGlFKUaBVNIQFoFkdAkOppnctXgnV9lChoBmgJaA9DCMNkqmBUbmtAlIaUUpRoFU1mAWgWR0CQ69863iJgdX2UKGgGaAloD0MIbazEPKuKckCUhpRSlGgVTUABaBZHQJDr7zFuNxV1fZQoaAZoCWgPQwg/OnXls0lyQJSGlFKUaBVNSgFoFkdAkOw8F+uvEHV9lChoBmgJaA9DCN6Th4XaV3BAlIaUUpRoFU0YAWgWR0CQ7Smgam4zdX2UKGgGaAloD0MIRS+jWG6mbkCUhpRSlGgVTTcBaBZHQJDuJqEeyRl1fZQoaAZoCWgPQwjaklURbs9QQJSGlFKUaBVL3WgWR0CQ7nOKO1fFdX2UKGgGaAloD0MIJxWNtf8mcECUhpRSlGgVTUIBaBZHQJDu6/cnE2p1fZQoaAZoCWgPQwgBiLt6lcpxQJSGlFKUaBVNXwFoFkdAkO8+R9w3pHV9lChoBmgJaA9DCCy5isUvSXFAlIaUUpRoFU0xAWgWR0CQ73br1M/RdX2UKGgGaAloD0MI1ub/VQejckCUhpRSlGgVTWUBaBZHQJDwB4W1twd1fZQoaAZoCWgPQwiVfVcEf8htQJSGlFKUaBVNUgFoFkdAkPBBDkU9IXV9lChoBmgJaA9DCL1WQnfJLW9AlIaUUpRoFU1TAWgWR0CQ8Q1baAWjdX2UKGgGaAloD0MIE2ba/hU4b0CUhpRSlGgVTTMBaBZHQJDz1dld1Md1fZQoaAZoCWgPQwjYZmMlpsxwQJSGlFKUaBVNNgFoFkdAkPQ4vN/vv3V9lChoBmgJaA9DCPIGmPkOrj9AlIaUUpRoFUvtaBZHQJD1ihysCDF1fZQoaAZoCWgPQwhJS+XtSCFyQJSGlFKUaBVNZQFoFkdAkPXr8iwB53V9lChoBmgJaA9DCDM2dLM/r3JAlIaUUpRoFU0qAWgWR0CQ9slNUOurdX2UKGgGaAloD0MIysABLd0hcECUhpRSlGgVTUsBaBZHQJD28MYuTRp1fZQoaAZoCWgPQwhznrEvmQ9wQJSGlFKUaBVNZQFoFkdAkPeQprk8zXV9lChoBmgJaA9DCOc1dolqoW1AlIaUUpRoFU0uAWgWR0CQ+MWdmQKbdX2UKGgGaAloD0MIurvOhnxcb0CUhpRSlGgVTSMBaBZHQJD5CcawUxp1fZQoaAZoCWgPQwgcKVsk7TpOQJSGlFKUaBVNAQFoFkdAkPnWwmmcfHV9lChoBmgJaA9DCMkFZ/C3nnFAlIaUUpRoFU1fAWgWR0CQ+fBrN4Z/dX2UKGgGaAloD0MIt5kK8cg2cECUhpRSlGgVTdYBaBZHQJD7W+36Q/51fZQoaAZoCWgPQwh319mQ/y1uQJSGlFKUaBVNWQFoFkdAkPvV1KXfInV9lChoBmgJaA9DCIaSyamdo1pAlIaUUpRoFU3oA2gWR0CQ++U0Nz8xdX2UKGgGaAloD0MIxAd2/BcWRECUhpRSlGgVS7loFkdAkPwMQVbiZXV9lChoBmgJaA9DCK/NxkpMO25AlIaUUpRoFU13AWgWR0CQ/HhMrVe8dX2UKGgGaAloD0MIrkhMUEPkbkCUhpRSlGgVTSwBaBZHQJD+UYDTz/Z1fZQoaAZoCWgPQwjgLCXLSS5OQJSGlFKUaBVNBwFoFkdAkP99PLxI8XV9lChoBmgJaA9DCAjNrnurIXFAlIaUUpRoFU1lAWgWR0CQ/9p8neBQdX2UKGgGaAloD0MIMQvtnKYYcUCUhpRSlGgVTS4BaBZHQJEBcuHvc8F1fZQoaAZoCWgPQwh/EwoRMIxwQJSGlFKUaBVNXAFoFkdAkQGT/yXlbXVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
ppo-LunarLander-v2.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:78bbba86fcc29298e7711874184515dbdf590ea244053bfc53993b3ef1bf9c83
|
| 3 |
+
size 147210
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
1.6.2
|
ppo-LunarLander-v2/data
ADDED
|
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"policy_class": {
|
| 3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
| 4 |
+
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
| 5 |
+
"__module__": "stable_baselines3.common.policies",
|
| 6 |
+
"__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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 ActorCriticPolicy.__init__ at 0x7f8ae5a3b3a0>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8ae5a3b430>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8ae5a3b4c0>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8ae5a3b550>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f8ae5a3b5e0>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f8ae5a3b670>",
|
| 13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8ae5a3b700>",
|
| 14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f8ae5a3b790>",
|
| 15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8ae5a3b820>",
|
| 16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8ae5a3b8b0>",
|
| 17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8ae5a3b940>",
|
| 18 |
+
"__abstractmethods__": "frozenset()",
|
| 19 |
+
"_abc_impl": "<_abc_data object at 0x7f8ae5a30fc0>"
|
| 20 |
+
},
|
| 21 |
+
"verbose": 1,
|
| 22 |
+
"policy_kwargs": {},
|
| 23 |
+
"observation_space": {
|
| 24 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
| 25 |
+
":serialized:": "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",
|
| 26 |
+
"dtype": "float32",
|
| 27 |
+
"_shape": [
|
| 28 |
+
8
|
| 29 |
+
],
|
| 30 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
| 31 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
| 32 |
+
"bounded_below": "[False False False False False False False False]",
|
| 33 |
+
"bounded_above": "[False False False False False False False False]",
|
| 34 |
+
"_np_random": null
|
| 35 |
+
},
|
| 36 |
+
"action_space": {
|
| 37 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
| 38 |
+
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
| 39 |
+
"n": 4,
|
| 40 |
+
"_shape": [],
|
| 41 |
+
"dtype": "int64",
|
| 42 |
+
"_np_random": null
|
| 43 |
+
},
|
| 44 |
+
"n_envs": 16,
|
| 45 |
+
"num_timesteps": 1015808,
|
| 46 |
+
"_total_timesteps": 1000000,
|
| 47 |
+
"_num_timesteps_at_start": 0,
|
| 48 |
+
"seed": null,
|
| 49 |
+
"action_noise": null,
|
| 50 |
+
"start_time": 1672248108033923646,
|
| 51 |
+
"learning_rate": 0.0003,
|
| 52 |
+
"tensorboard_log": null,
|
| 53 |
+
"lr_schedule": {
|
| 54 |
+
":type:": "<class 'function'>",
|
| 55 |
+
":serialized:": "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"
|
| 56 |
+
},
|
| 57 |
+
"_last_obs": {
|
| 58 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 59 |
+
":serialized:": "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"
|
| 60 |
+
},
|
| 61 |
+
"_last_episode_starts": {
|
| 62 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 63 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
| 64 |
+
},
|
| 65 |
+
"_last_original_obs": null,
|
| 66 |
+
"_episode_num": 0,
|
| 67 |
+
"use_sde": false,
|
| 68 |
+
"sde_sample_freq": -1,
|
| 69 |
+
"_current_progress_remaining": -0.015808000000000044,
|
| 70 |
+
"ep_info_buffer": {
|
| 71 |
+
":type:": "<class 'collections.deque'>",
|
| 72 |
+
":serialized:": "gAWVeRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIke9S6lLhcUCUhpRSlIwBbJRNkgGMAXSUR0CQlVq8DjiodX2UKGgGaAloD0MIYOY7+ImlcUCUhpRSlGgVTW0BaBZHQJCWYqnWJ791fZQoaAZoCWgPQwhuh4bFqEZwQJSGlFKUaBVNDgJoFkdAkJZ6//NqxnV9lChoBmgJaA9DCHDqA8m7EXBAlIaUUpRoFU10AWgWR0CQlrnSv1UVdX2UKGgGaAloD0MI+G2I8RolbkCUhpRSlGgVTZsBaBZHQJCsHpNbkfd1fZQoaAZoCWgPQwimRuhn6sJxQJSGlFKUaBVNPAFoFkdAkKx1tbcGknV9lChoBmgJaA9DCOT1YFJ8f21AlIaUUpRoFU1MAWgWR0CQrHbfP5YYdX2UKGgGaAloD0MIo7CLogcCckCUhpRSlGgVTZMBaBZHQJCsk3bVSXN1fZQoaAZoCWgPQwjji/Z4YdVwQJSGlFKUaBVNcAFoFkdAkK5iAH3UQXV9lChoBmgJaA9DCK6BrRIsuExAlIaUUpRoFU0KAWgWR0CQrx/Ot4iYdX2UKGgGaAloD0MIUMQihl3YcUCUhpRSlGgVTU0BaBZHQJCvp6PbO/t1fZQoaAZoCWgPQwhoeR7cnYdyQJSGlFKUaBVNUQFoFkdAkK+4YJmdy3V9lChoBmgJaA9DCHujVph+4nBAlIaUUpRoFU1CAWgWR0CQsLghr30xdX2UKGgGaAloD0MI0hqDTgifRkCUhpRSlGgVS+5oFkdAkLDC1NQCS3V9lChoBmgJaA9DCFzLZDie13FAlIaUUpRoFU2WAWgWR0CQsPNlRP43dX2UKGgGaAloD0MIhEcbR+xxcECUhpRSlGgVTUoBaBZHQJCxObsniNt1fZQoaAZoCWgPQwjyQc9m1RcnQJSGlFKUaBVNEQFoFkdAkLGXTd+G5HV9lChoBmgJaA9DCC/5n/xdempAlIaUUpRoFU06AWgWR0CQsqbMHKOldX2UKGgGaAloD0MIg9pv7YSncECUhpRSlGgVTXgBaBZHQJCzk1P3ztl1fZQoaAZoCWgPQwhBYVCmUSZuQJSGlFKUaBVNlgFoFkdAkLRvIsAeaXV9lChoBmgJaA9DCE+sU+V7u2xAlIaUUpRoFU1VAWgWR0CQt1KGtZFHdX2UKGgGaAloD0MIGcVyS+vBcUCUhpRSlGgVTSgBaBZHQJC4BmXgLql1fZQoaAZoCWgPQwhj00ohkLZrQJSGlFKUaBVNdAFoFkdAkLgy6tknTnV9lChoBmgJaA9DCHSZmgTvV3BAlIaUUpRoFU1rAWgWR0CQuEM23rledX2UKGgGaAloD0MIwAMDCB/QbkCUhpRSlGgVTXABaBZHQJC4keKbayt1fZQoaAZoCWgPQwjAWyBBcbFxQJSGlFKUaBVNJQFoFkdAkLix8twrD3V9lChoBmgJaA9DCMobYOY7TD1AlIaUUpRoFU0bAWgWR0CQunyBTXJ6dX2UKGgGaAloD0MIlgUTf5SycECUhpRSlGgVTUABaBZHQJC7Uu+RHPN1fZQoaAZoCWgPQwjiWu1hr6xxQJSGlFKUaBVNWwFoFkdAkLyAntv4unV9lChoBmgJaA9DCAVvSKOClG1AlIaUUpRoFU1iAWgWR0CQvIwg1WKedX2UKGgGaAloD0MIQ46tZ4h5bECUhpRSlGgVTYABaBZHQJC8hwxWT5h1fZQoaAZoCWgPQwjvObAc4chwQJSGlFKUaBVNJgFoFkdAkL2lvAGjbnV9lChoBmgJaA9DCBu8r8qFtm1AlIaUUpRoFU1PAWgWR0CQvgaM72csdX2UKGgGaAloD0MIuYrFbwoSbECUhpRSlGgVTUcBaBZHQJC/pr1uivh1fZQoaAZoCWgPQwj59NiWAT8vQJSGlFKUaBVL92gWR0CQwGn27FsIdX2UKGgGaAloD0MIJjj1geQoUECUhpRSlGgVS/BoFkdAkMCmznied3V9lChoBmgJaA9DCMNjP4ulT3FAlIaUUpRoFU3YAWgWR0CQwTBIFvAHdX2UKGgGaAloD0MIcvp6vma7cECUhpRSlGgVTRUBaBZHQJDBjVurIYF1fZQoaAZoCWgPQwgIdCZtKqhvQJSGlFKUaBVNIwFoFkdAkMIFOwgTy3V9lChoBmgJaA9DCIS7s3Zb6WxAlIaUUpRoFU1UAWgWR0CQwuFGXokidX2UKGgGaAloD0MI/tKiPsmlcECUhpRSlGgVTWABaBZHQJDEa04R28t1fZQoaAZoCWgPQwjxuKgWEcXeP5SGlFKUaBVNCAFoFkdAkMUH5WRzR3V9lChoBmgJaA9DCFFPH4F/BXBAlIaUUpRoFU0xAWgWR0CQxVz6rNnodX2UKGgGaAloD0MICrq9pDEyQUCUhpRSlGgVS+BoFkdAkMcfT5O8CnV9lChoBmgJaA9DCCaL+49MhW5AlIaUUpRoFU1NAWgWR0CQx3ncL0BfdX2UKGgGaAloD0MIP1WFBuLCbkCUhpRSlGgVTVoBaBZHQJDH+fWcz691fZQoaAZoCWgPQwi0rWad8XNxQJSGlFKUaBVNmwFoFkdAkMhKHoHLR3V9lChoBmgJaA9DCJuvko9dBXJAlIaUUpRoFU06AWgWR0CQyHfIjnmrdX2UKGgGaAloD0MIUp55OWw5a0CUhpRSlGgVTUUBaBZHQJDIeObRWtF1fZQoaAZoCWgPQwh90LNZdYltQJSGlFKUaBVNRAFoFkdAkMzbFS88LnV9lChoBmgJaA9DCDbJj/iVrG9AlIaUUpRoFU09AWgWR0CQzT4Z/CqIdX2UKGgGaAloD0MIGQPrOH7QcECUhpRSlGgVTXEBaBZHQJDNqJ/G2kV1fZQoaAZoCWgPQwhDN/sDZSdwQJSGlFKUaBVNPgFoFkdAkODFUEPlMnV9lChoBmgJaA9DCLmI78SsZ3FAlIaUUpRoFU2VAWgWR0CQ4SsY2sJZdX2UKGgGaAloD0MItHHEWvx6cECUhpRSlGgVTX8BaBZHQJDhNcgQpWp1fZQoaAZoCWgPQwjECUyndQBuQJSGlFKUaBVNRwFoFkdAkOPIznA6+3V9lChoBmgJaA9DCGpq2VoflnBAlIaUUpRoFU0aAWgWR0CQ4/1dgOSXdX2UKGgGaAloD0MIJHuEmiF7QUCUhpRSlGgVS/toFkdAkOQNqDbrT3V9lChoBmgJaA9DCFtB0xIrOV1AlIaUUpRoFU3oA2gWR0CQ5G1s+FDfdX2UKGgGaAloD0MIq+l6ouuGQECUhpRSlGgVTQQBaBZHQJDkhYZEUj91fZQoaAZoCWgPQwi9j6M5MuZxQJSGlFKUaBVNgAFoFkdAkOVfMW43FXV9lChoBmgJaA9DCPYn8blTBHFAlIaUUpRoFU2XAWgWR0CQ5YnBtUGWdX2UKGgGaAloD0MIn1bRH9qKckCUhpRSlGgVTUoBaBZHQJDmONCJGfB1fZQoaAZoCWgPQwhIUz2Zf19vQJSGlFKUaBVNVwFoFkdAkOb5QDV6NXV9lChoBmgJaA9DCGsLz0tFpXBAlIaUUpRoFU0rAWgWR0CQ6hrfLs8gdX2UKGgGaAloD0MINZawNkZwcUCUhpRSlGgVTUUBaBZHQJDqZiay8jB1fZQoaAZoCWgPQwjnq+Rj9xxxQJSGlFKUaBVNIQFoFkdAkOppnctXgnV9lChoBmgJaA9DCMNkqmBUbmtAlIaUUpRoFU1mAWgWR0CQ69863iJgdX2UKGgGaAloD0MIbazEPKuKckCUhpRSlGgVTUABaBZHQJDr7zFuNxV1fZQoaAZoCWgPQwg/OnXls0lyQJSGlFKUaBVNSgFoFkdAkOw8F+uvEHV9lChoBmgJaA9DCN6Th4XaV3BAlIaUUpRoFU0YAWgWR0CQ7Smgam4zdX2UKGgGaAloD0MIRS+jWG6mbkCUhpRSlGgVTTcBaBZHQJDuJqEeyRl1fZQoaAZoCWgPQwjaklURbs9QQJSGlFKUaBVL3WgWR0CQ7nOKO1fFdX2UKGgGaAloD0MIJxWNtf8mcECUhpRSlGgVTUIBaBZHQJDu6/cnE2p1fZQoaAZoCWgPQwgBiLt6lcpxQJSGlFKUaBVNXwFoFkdAkO8+R9w3pHV9lChoBmgJaA9DCCy5isUvSXFAlIaUUpRoFU0xAWgWR0CQ73br1M/RdX2UKGgGaAloD0MI1ub/VQejckCUhpRSlGgVTWUBaBZHQJDwB4W1twd1fZQoaAZoCWgPQwiVfVcEf8htQJSGlFKUaBVNUgFoFkdAkPBBDkU9IXV9lChoBmgJaA9DCL1WQnfJLW9AlIaUUpRoFU1TAWgWR0CQ8Q1baAWjdX2UKGgGaAloD0MIE2ba/hU4b0CUhpRSlGgVTTMBaBZHQJDz1dld1Md1fZQoaAZoCWgPQwjYZmMlpsxwQJSGlFKUaBVNNgFoFkdAkPQ4vN/vv3V9lChoBmgJaA9DCPIGmPkOrj9AlIaUUpRoFUvtaBZHQJD1ihysCDF1fZQoaAZoCWgPQwhJS+XtSCFyQJSGlFKUaBVNZQFoFkdAkPXr8iwB53V9lChoBmgJaA9DCDM2dLM/r3JAlIaUUpRoFU0qAWgWR0CQ9slNUOurdX2UKGgGaAloD0MIysABLd0hcECUhpRSlGgVTUsBaBZHQJD28MYuTRp1fZQoaAZoCWgPQwhznrEvmQ9wQJSGlFKUaBVNZQFoFkdAkPeQprk8zXV9lChoBmgJaA9DCOc1dolqoW1AlIaUUpRoFU0uAWgWR0CQ+MWdmQKbdX2UKGgGaAloD0MIurvOhnxcb0CUhpRSlGgVTSMBaBZHQJD5CcawUxp1fZQoaAZoCWgPQwgcKVsk7TpOQJSGlFKUaBVNAQFoFkdAkPnWwmmcfHV9lChoBmgJaA9DCMkFZ/C3nnFAlIaUUpRoFU1fAWgWR0CQ+fBrN4Z/dX2UKGgGaAloD0MIt5kK8cg2cECUhpRSlGgVTdYBaBZHQJD7W+36Q/51fZQoaAZoCWgPQwh319mQ/y1uQJSGlFKUaBVNWQFoFkdAkPvV1KXfInV9lChoBmgJaA9DCIaSyamdo1pAlIaUUpRoFU3oA2gWR0CQ++U0Nz8xdX2UKGgGaAloD0MIxAd2/BcWRECUhpRSlGgVS7loFkdAkPwMQVbiZXV9lChoBmgJaA9DCK/NxkpMO25AlIaUUpRoFU13AWgWR0CQ/HhMrVe8dX2UKGgGaAloD0MIrkhMUEPkbkCUhpRSlGgVTSwBaBZHQJD+UYDTz/Z1fZQoaAZoCWgPQwjgLCXLSS5OQJSGlFKUaBVNBwFoFkdAkP99PLxI8XV9lChoBmgJaA9DCAjNrnurIXFAlIaUUpRoFU1lAWgWR0CQ/9p8neBQdX2UKGgGaAloD0MIMQvtnKYYcUCUhpRSlGgVTS4BaBZHQJEBcuHvc8F1fZQoaAZoCWgPQwh/EwoRMIxwQJSGlFKUaBVNXAFoFkdAkQGT/yXlbXVlLg=="
|
| 73 |
+
},
|
| 74 |
+
"ep_success_buffer": {
|
| 75 |
+
":type:": "<class 'collections.deque'>",
|
| 76 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
| 77 |
+
},
|
| 78 |
+
"_n_updates": 248,
|
| 79 |
+
"n_steps": 1024,
|
| 80 |
+
"gamma": 0.999,
|
| 81 |
+
"gae_lambda": 0.98,
|
| 82 |
+
"ent_coef": 0.01,
|
| 83 |
+
"vf_coef": 0.5,
|
| 84 |
+
"max_grad_norm": 0.5,
|
| 85 |
+
"batch_size": 64,
|
| 86 |
+
"n_epochs": 4,
|
| 87 |
+
"clip_range": {
|
| 88 |
+
":type:": "<class 'function'>",
|
| 89 |
+
":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
|
| 90 |
+
},
|
| 91 |
+
"clip_range_vf": null,
|
| 92 |
+
"normalize_advantage": true,
|
| 93 |
+
"target_kl": null
|
| 94 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a99b3943865efa5d52b348a3b47d6ac15fd4d92199fd89e1343183bca34b871f
|
| 3 |
+
size 87929
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5014094ba619bc4ff30e08cb2de8dc6db2ea04594584383084cf43a0978e084d
|
| 3 |
+
size 43201
|
ppo-LunarLander-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
|
ppo-LunarLander-v2/system_info.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
|
| 2 |
+
Python: 3.8.16
|
| 3 |
+
Stable-Baselines3: 1.6.2
|
| 4 |
+
PyTorch: 1.13.0+cu116
|
| 5 |
+
GPU Enabled: True
|
| 6 |
+
Numpy: 1.21.6
|
| 7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
|
Binary file (236 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 235.07251226427465, "std_reward": 44.795443140496836, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-28T17:48:09.736975"}
|