File size: 3,019 Bytes
15477d2 fe11896 15477d2 fe11896 15477d2 |
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 |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.74 +/- 0.27
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
#install
!apt install python-opengl
!apt install ffmpeg
!apt install xvfb
!pip3 install pyvirtualdisplay
!pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit6/requirements-unit6.txt
# Virtual display
from pyvirtualdisplay import Display
virtual_display = Display(visible=0, size=(1400, 900))
virtual_display.start()
#imports
import pybullet_envs
import panda_gym
import gym
import os
from huggingface_sb3 import load_from_hub, package_to_hub
from stable_baselines3 import A2C
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
from stable_baselines3.common.env_util import make_vec_env
from huggingface_hub import notebook_login
#Define the environment called "PandaReachDense-v2"
env_id = "PandaReachDense-v2"
#Make a vectorized environment
env = make_vec_env(env_id, n_envs=4)
#Add a wrapper to normalize the observations and rewards. Check the documentation
env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10)
#Create the A2C Model (don't forget verbose=1 to print the training logs).
model = A2C(policy = "MultiInputPolicy",
env = env,
gae_lambda = 0.9,
gamma = 0.95,
learning_rate = 0.001,
max_grad_norm = 0.5,
n_steps = 8,
vf_coef = 0.4,
ent_coef = 0.0,
seed=11,
policy_kwargs=dict(
log_std_init=-2, ortho_init=False),
normalize_advantage=False,
use_rms_prop= True,
use_sde= True,
verbose=1)
#Train it for 1M Timesteps
model.learn(1_500_000)
#Save the model and VecNormalize statistics when saving the agent
model.save(f"a2c-{env_id}")
env.save(f"vec_normalize_{env_id}.pkl")
#Evaluate your agent
eval_env = DummyVecEnv([lambda: gym.make(env_id)])
eval_env = VecNormalize.load(f"vec_normalize_{env_id}.pkl", eval_env)
# do not update them at test time
eval_env.training = False
# reward normalization is not needed at test time
eval_env.norm_reward = False
# Load the model
model = A2C.load(f"a2c-{env_id}")
#Evaluate model
mean_reward, std_reward = evaluate_policy(model, eval_env)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
...
```
|