# **PPO** Agent playing **procgen-coinrun-easy**

This is a trained model of a **PPO** agent playing **procgen-coinrun-easy** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo.

All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/f3w1hwyb.

## Training Results

This model was trained from 3 trainings of **PPO** agents using different initial seeds. These agents were trained by checking out [21ee1ab](https://github.com/sgoodfriend/rl-algo-impls/tree/21ee1ab96a186676e5ed2f8c3185902f7c7bca7a). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std).

| algo   | env     |   seed |   reward_mean |   reward_std |   eval_episodes | best   | wandb_url                                                                    |
|:-------|:--------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------|
| ppo    | coinrun |      1 |        9.0625 |      2.91481 |              64 |        | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/6vwst93s) |
| ppo    | coinrun |      2 |        9.0625 |      2.91481 |              64 | *      | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/vmjd3amn) |
| ppo    | coinrun |      3 |        8.125  |      3.90312 |              64 |        | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/3sqxjicx) |


### Prerequisites: Weights & Biases (WandB)
Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.

Before doing anything below, you'll need to create a wandb account and run `wandb
login`.



## Usage
/sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls

Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
[21ee1ab](https://github.com/sgoodfriend/rl-algo-impls/tree/21ee1ab96a186676e5ed2f8c3185902f7c7bca7a).
```
# Downloads the model, sets hyperparameters, and runs agent for 3 episodes
python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/vmjd3amn
```

Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb)
notebook.



## Training
If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: [21ee1ab](https://github.com/sgoodfriend/rl-algo-impls/tree/21ee1ab96a186676e5ed2f8c3185902f7c7bca7a). While
training is deterministic, different hardware will give different results.

```
python train.py --algo ppo --env procgen-coinrun-easy --seed 2
```

Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
[colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb)
notebook.



## Benchmarking (with Lambda Labs instance)
This and other models from https://api.wandb.ai/links/sgoodfriend/f3w1hwyb were generated by running a script on a Lambda
Labs instance. In a Lambda Labs instance terminal:
```
git clone git@github.com:sgoodfriend/rl-algo-impls.git
cd rl-algo-impls
bash ./lambda_labs/setup.sh
wandb login
bash ./lambda_labs/benchmark.sh
```

### Alternative: Google Colab Pro+
As an alternative,
[colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb),
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.



## Hyperparameters
This isn't exactly the format of hyperparams in hyperparams/ppo.yml, but instead the Wandb Run Config. However, it's very
close and has some additional data:
```
algo: ppo
algo_hyperparams:
  batch_size: 2048
  clip_range: 0.2
  clip_range_vf: 0.2
  ent_coef: 0.01
  gae_lambda: 0.95
  gamma: 0.999
  learning_rate: 0.0005
  n_epochs: 3
  n_steps: 256
  vf_coef: 0.5
env: procgen-coinrun-easy
env_hyperparams:
  is_procgen: true
  make_kwargs:
    distribution_mode: easy
  n_envs: 64
  normalize: true
env_id: coinrun
eval_params:
  deterministic: false
  ignore_first_episode: true
n_timesteps: 25000000
policy_hyperparams:
  activation_fn: relu
  cnn_feature_dim: 256
  cnn_layers_init_orthogonal: false
  cnn_style: impala
  init_layers_orthogonal: true
seed: 2
use_deterministic_algorithms: true
wandb_entity: null
wandb_project_name: rl-algo-impls-benchmarks
wandb_tags:
- benchmark_21ee1ab
- host_138-2-238-100

```

{"library_name": "rl-algo-impls", "tags": ["procgen-coinrun-easy", "ppo", "deep-reinforcement-learning", "reinforcement-learning"], "model-index": [{"name": "ppo", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "procgen-coinrun-easy", "type": "procgen-coinrun-easy"}, "metrics": [{"type": "mean_reward", "value": "9.06 +/- 2.91", "name": "mean_reward"}]}]}]}
rl-algo-impls procgen-coinrun-easy ppo deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#rl-algo-impls #procgen-coinrun-easy #ppo #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 

PPO Agent playing procgen-coinrun-easy
======================================


This is a trained model of a PPO agent playing procgen-coinrun-easy using the /sgoodfriend/rl-algo-impls repo.


All models trained at this commit can be found at URL


Training Results
----------------


This model was trained from 3 trainings of PPO agents using different initial seeds. These agents were trained by checking out 21ee1ab. The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std).



### Prerequisites: Weights & Biases (WandB)


Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.


Before doing anything below, you'll need to create a wandb account and run 'wandb
login'.


Usage
-----


/sgoodfriend/rl-algo-impls: URL


Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
21ee1ab.


Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
colab\_enjoy.ipynb
notebook.


Training
--------


If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: 21ee1ab. While
training is deterministic, different hardware will give different results.


Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
colab\_train.ipynb
notebook.


Benchmarking (with Lambda Labs instance)
----------------------------------------


This and other models from URL were generated by running a script on a Lambda
Labs instance. In a Lambda Labs instance terminal:


### Alternative: Google Colab Pro+


As an alternative,
colab\_benchmark.ipynb,
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.


Hyperparameters
---------------


This isn't exactly the format of hyperparams in hyperparams/URL, but instead the Wandb Run Config. However, it's very
close and has some additional data:



### Prerequisites: Weights & Biases (WandB)


Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.


Before doing anything below, you'll need to create a wandb account and run 'wandb
login'.


Usage
-----


/sgoodfriend/rl-algo-impls: URL


Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
21ee1ab.


Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
colab\_enjoy.ipynb
notebook.


Training
--------


If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: 21ee1ab. While
training is deterministic, different hardware will give different results.


Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
colab\_train.ipynb
notebook.


Benchmarking (with Lambda Labs instance)
----------------------------------------


This and other models from URL were generated by running a script on a Lambda
Labs instance. In a Lambda Labs instance terminal: ### Alternative: Google Colab Pro+


As an alternative,
colab\_benchmark.ipynb,
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.


Hyperparameters
---------------


This isn't exactly the format of hyperparams in hyperparams/URL, but instead the Wandb Run Config. However, it's very
close and has some additional data:
TAGS
#rl-algo-impls #procgen-coinrun-easy #ppo #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 ### Prerequisites: Weights & Biases (WandB)


Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.


Before doing anything below, you'll need to create a wandb account and run 'wandb
login'.


Usage
-----


/sgoodfriend/rl-algo-impls: URL


Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
21ee1ab.


Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
colab\_enjoy.ipynb
notebook.


Training
--------


If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: 21ee1ab. While
training is deterministic, different hardware will give different results.


Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
colab\_train.ipynb
notebook.


Benchmarking (with Lambda Labs instance)
----------------------------------------


This and other models from URL were generated by running a script on a Lambda
Labs instance. In a Lambda Labs instance terminal: ### Alternative: Google Colab Pro+


As an alternative,
colab\_benchmark.ipynb,
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.


Hyperparameters
---------------


This isn't exactly the format of hyperparams in hyperparams/URL, but instead the Wandb Run Config. However, it's very
close and has some additional data:
TAGS
#rl-algo-impls #procgen-coinrun-easy #ppo #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
### Prerequisites: Weights & Biases (WandB)


Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.


Before doing anything below, you'll need to create a wandb account and run 'wandb
login'.


Usage
-----


/sgoodfriend/rl-algo-impls: URL


Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
21ee1ab.


Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
colab\_enjoy.ipynb
notebook.


Training
--------


If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: 21ee1ab. While
training is deterministic, different hardware will give different results.


Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
colab\_train.ipynb
notebook.


Benchmarking (with Lambda Labs instance)
----------------------------------------


This and other models from URL were generated by running a script on a Lambda
Labs instance. In a Lambda Labs instance terminal:### Alternative: Google Colab Pro+


As an alternative,
colab\_benchmark.ipynb,
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.


Hyperparameters
---------------


This isn't exactly the format of hyperparams in hyperparams/URL, but instead the Wandb Run Config. However, it's very
close and has some additional data: