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---
library_name: stable-baselines3
tags:
- BipedalWalker-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- metrics:
- type: mean_reward
value: 332.83 +/- 0.42
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalker-v3
type: BipedalWalker-v3
---
# **TQC** Agent playing **BipedalWalker-v3**
This is a trained model of a **TQC** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
from huggingface_sb3 import load_from_hub
from sb3_contrib import TQC
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
# Download checkpoint
checkpoint = load_from_hub("araffin/tqc-BipedalWalker-v3", "tqc-BipedalWalker-v3.zip")
# Load the model
model = TQC.load(checkpoint)
env = make_vec_env("BipedalWalker-v3", n_envs=1)
# Evaluate
print("Evaluating model")
mean_reward, std_reward = evaluate_policy(
model,
env,
n_eval_episodes=20,
deterministic=True,
)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
# Start a new episode
obs = env.reset()
try:
while True:
action, _states = model.predict(obs, deterministic=True)
obs, rewards, dones, info = env.step(action)
env.render()
except KeyboardInterrupt:
pass
```
## Training code (with SB3)
```python
from sb3_contrib import TQC
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.callbacks import EvalCallback
# Create the environment
env_id = "BipedalWalker-v3"
n_envs = 2
env = make_vec_env(env_id, n_envs=n_envs)
# Create the evaluation envs
eval_envs = make_vec_env(env_id, n_envs=5)
# Adjust evaluation interval depending on the number of envs
eval_freq = int(1e5)
eval_freq = max(eval_freq // n_envs, 1)
# Create evaluation callback to save best model
# and monitor agent performance
eval_callback = EvalCallback(
eval_envs,
best_model_save_path="./logs/",
eval_freq=eval_freq,
n_eval_episodes=10,
)
# Instantiate the agent
# Hyperparameters from https://github.com/DLR-RM/rl-baselines3-zoo
model = TQC(
"MlpPolicy",
env,
learning_starts=10000,
batch_size=256,
buffer_size=300000,
learning_rate=7.3e-4,
# gSDE is from https://proceedings.mlr.press/v164/raffin22a.html
use_sde=True,
train_freq=8,
gradient_steps=8,
gamma=0.98,
tau=0.02,
policy_kwargs=dict(log_std_init=-3, net_arch=[400, 300]),
verbose=1,
)
# Train the agent (you can kill it before using ctrl+c)
try:
model.learn(total_timesteps=int(5e5), callback=eval_callback, log_interval=10)
except KeyboardInterrupt:
pass
```
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