DarsiniRam
commited on
Commit
•
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Parent(s):
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Initial commit
Browse files- .gitattributes +1 -0
- README.md +83 -0
- args.yml +83 -0
- config.yml +25 -0
- dqn-FrozenLake-v1.zip +3 -0
- dqn-FrozenLake-v1/_stable_baselines3_version +1 -0
- dqn-FrozenLake-v1/data +117 -0
- dqn-FrozenLake-v1/policy.optimizer.pth +3 -0
- dqn-FrozenLake-v1/policy.pth +3 -0
- dqn-FrozenLake-v1/pytorch_variables.pth +3 -0
- dqn-FrozenLake-v1/system_info.txt +9 -0
- env_kwargs.yml +1 -0
- replay.mp4 +0 -0
- results.json +1 -0
- train_eval_metrics.zip +3 -0
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ 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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*tfevents* 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
ADDED
@@ -0,0 +1,83 @@
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---
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library_name: stable-baselines3
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tags:
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- FrozenLake-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: DQN
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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: FrozenLake-v1
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type: FrozenLake-v1
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metrics:
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- type: mean_reward
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value: 0.50 +/- 0.50
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name: mean_reward
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verified: false
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---
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# **DQN** Agent playing **FrozenLake-v1**
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This is a trained model of a **DQN** agent playing **FrozenLake-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 dqn --env FrozenLake-v1 -orga DarsiniRam -f logs/
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python -m rl_zoo3.enjoy --algo dqn --env FrozenLake-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 dqn --env FrozenLake-v1 -orga DarsiniRam -f logs/
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python -m rl_zoo3.enjoy --algo dqn --env FrozenLake-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 dqn --env FrozenLake-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 dqn --env FrozenLake-v1 -f logs/ -orga DarsiniRam
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```
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## Hyperparameters
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```python
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OrderedDict([('batch_size', 32),
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('buffer_size', 100000),
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('exploration_final_eps', 0.01),
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('exploration_fraction', 0.1),
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('gradient_steps', 1),
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('learning_rate', 0.0001),
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('learning_starts', 1000),
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('n_timesteps', 100000.0),
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('optimize_memory_usage', False),
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('policy', 'MlpPolicy'),
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('target_update_interval', 1000),
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('train_freq', 4),
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('normalize', False)])
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```
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# Environment Arguments
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```python
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{'render_mode': 'rgb_array'}
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```
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args.yml
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!!python/object/apply:collections.OrderedDict
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- - - algo
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- dqn
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- - conf_file
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- fl_dqn.yml
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- - device
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- auto
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- - env
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- FrozenLake-v1
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- - env_kwargs
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- null
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- - eval_env_kwargs
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- null
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- - eval_episodes
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- 5
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- - eval_freq
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- 25000
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- - gym_packages
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- []
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- - hyperparams
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- null
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- - log_folder
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- fl_logs/
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- - log_interval
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- -1
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- - max_total_trials
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+
- null
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+
- - n_eval_envs
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- 1
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- - n_evaluations
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- null
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+
- - n_jobs
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- 1
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+
- - n_startup_trials
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+
- 10
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- - n_timesteps
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- -1
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- - n_trials
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+
- 500
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+
- - no_optim_plots
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- false
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+
- - num_threads
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+
- -1
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+
- - optimization_log_path
|
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+
- null
|
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+
- - optimize_hyperparameters
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+
- false
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+
- - progress
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+
- false
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- - pruner
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+
- median
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+
- - sampler
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+
- tpe
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+
- - save_freq
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+
- -1
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+
- - save_replay_buffer
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+
- false
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+
- - seed
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+
- 363105297
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+
- - storage
|
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+
- null
|
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+
- - study_name
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+
- null
|
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+
- - tensorboard_log
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+
- ''
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+
- - track
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67 |
+
- false
|
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+
- - trained_agent
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+
- ''
|
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+
- - truncate_last_trajectory
|
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+
- true
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+
- - uuid
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+
- false
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+
- - vec_env
|
75 |
+
- dummy
|
76 |
+
- - verbose
|
77 |
+
- 1
|
78 |
+
- - wandb_entity
|
79 |
+
- null
|
80 |
+
- - wandb_project_name
|
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+
- sb3
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+
- - wandb_tags
|
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+
- []
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config.yml
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+
!!python/object/apply:collections.OrderedDict
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- - - batch_size
|
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+
- 32
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+
- - buffer_size
|
5 |
+
- 100000
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6 |
+
- - exploration_final_eps
|
7 |
+
- 0.01
|
8 |
+
- - exploration_fraction
|
9 |
+
- 0.1
|
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+
- - gradient_steps
|
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+
- 1
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+
- - learning_rate
|
13 |
+
- 0.0001
|
14 |
+
- - learning_starts
|
15 |
+
- 1000
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+
- - n_timesteps
|
17 |
+
- 100000.0
|
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+
- - optimize_memory_usage
|
19 |
+
- false
|
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+
- - policy
|
21 |
+
- MlpPolicy
|
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+
- - target_update_interval
|
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+
- 1000
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- - train_freq
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- 4
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dqn-FrozenLake-v1.zip
ADDED
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version https://git-lfs.github.com/spec/v1
|
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+
oid sha256:08fe51fb44171399f49981e3a6e150458e64959293da1d37b4a3c0b94248e020
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+
size 115453
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dqn-FrozenLake-v1/_stable_baselines3_version
ADDED
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2.4.0a7
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dqn-FrozenLake-v1/data
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{
|
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"policy_class": {
|
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+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLmRxbi5wb2xpY2llc5SMCURRTlBvbGljeZSTlC4=",
|
5 |
+
"__module__": "stable_baselines3.dqn.policies",
|
6 |
+
"__annotations__": "{'q_net': <class 'stable_baselines3.dqn.policies.QNetwork'>, 'q_net_target': <class 'stable_baselines3.dqn.policies.QNetwork'>}",
|
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+
"__doc__": "\n Policy class with Q-Value Net and target net for DQN\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 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 ",
|
8 |
+
"__init__": "<function DQNPolicy.__init__ at 0x785671403910>",
|
9 |
+
"_build": "<function DQNPolicy._build at 0x7856714039a0>",
|
10 |
+
"make_q_net": "<function DQNPolicy.make_q_net at 0x785671403a30>",
|
11 |
+
"forward": "<function DQNPolicy.forward at 0x785671403ac0>",
|
12 |
+
"_predict": "<function DQNPolicy._predict at 0x785671403b50>",
|
13 |
+
"_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x785671403be0>",
|
14 |
+
"set_training_mode": "<function DQNPolicy.set_training_mode at 0x785671403c70>",
|
15 |
+
"__abstractmethods__": "frozenset()",
|
16 |
+
"_abc_impl": "<_abc._abc_data object at 0x78567141c740>"
|
17 |
+
},
|
18 |
+
"verbose": 1,
|
19 |
+
"policy_kwargs": {},
|
20 |
+
"num_timesteps": 100000,
|
21 |
+
"_total_timesteps": 100000,
|
22 |
+
"_num_timesteps_at_start": 0,
|
23 |
+
"seed": 0,
|
24 |
+
"action_noise": null,
|
25 |
+
"start_time": 1728008909333600695,
|
26 |
+
"learning_rate": {
|
27 |
+
":type:": "<class 'function'>",
|
28 |
+
":serialized:": "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"
|
29 |
+
},
|
30 |
+
"tensorboard_log": null,
|
31 |
+
"_last_obs": null,
|
32 |
+
"_last_episode_starts": {
|
33 |
+
":type:": "<class 'numpy.ndarray'>",
|
34 |
+
":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAAGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
|
35 |
+
},
|
36 |
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|
|
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1 |
+
- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
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+
- Python: 3.10.12
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3 |
+
- Stable-Baselines3: 2.4.0a7
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4 |
+
- PyTorch: 2.4.1+cu121
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- GPU Enabled: True
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- Numpy: 1.26.4
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+
- Cloudpickle: 2.2.1
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8 |
+
- Gymnasium: 0.29.1
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9 |
+
- OpenAI Gym: 0.25.2
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env_kwargs.yml
ADDED
@@ -0,0 +1 @@
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+
render_mode: rgb_array
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replay.mp4
ADDED
Binary file (394 kB). View file
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results.json
ADDED
@@ -0,0 +1 @@
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+
{"mean_reward": 0.5, "std_reward": 0.5, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-10-04T02:34:53.648940"}
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train_eval_metrics.zip
ADDED
@@ -0,0 +1,3 @@
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+
version https://git-lfs.github.com/spec/v1
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2 |
+
oid sha256:1f7a043d46411856481100e9ed4a77ff0c7ede12d8e7ed1aebeae80161731a1f
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3 |
+
size 54564
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