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