File size: 5,497 Bytes
5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a 8bf4dee 5d5d65a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
---
library_name: rl-algo-impls
tags:
- MountainCarContinuous-v0
- vpg
- deep-reinforcement-learning
- reinforcement-learning
model-index:
- name: vpg
results:
- metrics:
- type: mean_reward
value: 90.87 +/- 27.43
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCarContinuous-v0
type: MountainCarContinuous-v0
---
# **VPG** Agent playing **MountainCarContinuous-v0**
This is a trained model of a **VPG** agent playing **MountainCarContinuous-v0** 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/09frjfcs.
## Training Results
This model was trained from 3 trainings of **VPG** agents using different initial seeds. These agents were trained by checking out [2067e21](https://github.com/sgoodfriend/rl-algo-impls/tree/2067e21d62fff5db60168687e7d9e89019a8bfc0). 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 |
|:-------|:-------------------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------|
| vpg | MountainCarContinuous-v0 | 1 | 90.8837 | 27.4807 | 12 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/pdlx5ase) |
| vpg | MountainCarContinuous-v0 | 2 | 82.4443 | 37.2333 | 12 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/6dg2esp1) |
| vpg | MountainCarContinuous-v0 | 3 | 90.869 | 27.4335 | 12 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/dvdk3wcw) |
### 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:
[2067e21](https://github.com/sgoodfriend/rl-algo-impls/tree/2067e21d62fff5db60168687e7d9e89019a8bfc0).
```
# Downloads the model, sets hyperparameters, and runs agent for 3 episodes
python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/dvdk3wcw
```
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: [2067e21](https://github.com/sgoodfriend/rl-algo-impls/tree/2067e21d62fff5db60168687e7d9e89019a8bfc0). While
training is deterministic, different hardware will give different results.
```
python train.py --algo vpg --env MountainCarContinuous-v0 --seed 3
```
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/09frjfcs 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 [-a {"ppo a2c dqn vpg"}] [-e ENVS] [-j {6}] [-p {rl-algo-impls-benchmarks}] [-s {"1 2 3"}]
```
### 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/vpg.yml, but instead the Wandb Run Config. However, it's very
close and has some additional data:
```
algo: vpg
algo_hyperparams:
gae_lambda: 0.9
gamma: 0.99
max_grad_norm: 5
n_steps: 1000
pi_lr: 0.0005
train_v_iters: 80
val_lr: 0.001
device: auto
env: MountainCarContinuous-v0
env_hyperparams:
n_envs: 4
normalize: true
env_id: null
eval_params:
step_freq: 5000
n_timesteps: 300000
policy_hyperparams: {}
seed: 3
use_deterministic_algorithms: true
wandb_entity: null
wandb_group: null
wandb_project_name: rl-algo-impls-benchmarks
wandb_tags:
- benchmark_2067e21
- host_155-248-199-228
```
|