# Basic Usage

## Two Ways to Run
After the dependencies have been installed, there are **two ways** to run 
the CleanRL script under the uv virtual environments.


1. Using `uv run`:

    ```bash
    uv run python cleanrl/ppo.py \
        --seed 1 \
        --env-id CartPole-v0 \
        --total-timesteps 50000
    ```
    <script id="asciicast-443649" src="https://asciinema.org/a/443649.js" async></script>


2. Using `uv venv`:

    1. We first activate the virtual environment by using
    `uv venv`
    2. Then, run any desired CleanRL script
   
    Attention: Each step must be executed separately!


    ```bash
    uv venv
    ```
    ```bash
    python cleanrl/ppo.py \
        --seed 1 \
        --env-id CartPole-v0 \
        --total-timesteps 50000
    ```
    <script id="asciicast-JL1FR00I2JNklAhMd2dwEAQuz" src="https://asciinema.org/a/JL1FR00I2JNklAhMd2dwEAQuz.js" async></script>

!!! note

    We recommend `uv venv` workflow for development. When the shell is activated, you should
    be seeing a prefix like `(cleanrl-iXg02GqF-py3.9)` in your shell's prompt, which is the name
    of the poetry's virtual environment.
    **We will assume to run other commands (e.g. `tensorboard`) in the documentation within the poetry's shell.**


!!! warning

    If you are using NVIDIA ampere GPUs (e.g., 3060 TI), you might meet the following error

    ```bash
    NVIDIA GeForce RTX 3060 Ti with CUDA capability sm_86 is not compatible with the current PyTorch installation.
    The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.
    If you want to use the NVIDIA GeForce RTX 3060 Ti GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

    warnings.warn(incompatible_device_warn.format(device_name, capability, " ".join(arch_list), device_name))
    Traceback (most recent call last):
    File "ppo_atari_envpool.py", line 240, in <module>
        action, logprob, _, value = agent.get_action_and_value(next_obs)
    File "ppo_atari_envpool.py", line 156, in get_action_and_value
        hidden = self.network(x / 255.0)
    RuntimeError: CUDA error: no kernel image is available for execution on the device
    CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
    For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
    ```

    This is because the `torch` wheel on PyPi is built with cuda 10.2. You would need to manually install the cuda 11.3 wheel like this:
    ```bash
    uv pip install torch==1.12.1 --upgrade --extra-index-url https://download.pytorch.org/whl/cu113
    ```
    Then, you can run the script again.

## Visualize Training Metrics

By default, the CleanRL scripts record all the training metrics via Tensorboard
into the `runs` folder. So, after running the training script above, feel free to run

```bash
tensorboard --logdir runs
```

![Tensorboard](tensorboard.png)


## Visualize the Agent's Gameplay Videos

CleanRL helps record the agent's gameplay videos with a `--capture_video` flag,
which will save the videos in the `videos/{$run_name}` folder.

```bash linenums="1" hl_lines="5"
python cleanrl/ppo.py \
    --seed 1 \
    --env-id CartPole-v0 \
    --total-timesteps 50000 \
    --capture_video
```

![videos](videos.png)
![videos2](videos2.png)

## Get Documentation

You can directly obtained the documentation by using the `--help` flag.

```bash
python cleanrl/ppo.py --help

usage: ppo.py [-h] [--exp-name EXP_NAME] [--env-id ENV_ID]
              [--learning-rate LEARNING_RATE] [--seed SEED]
              [--total-timesteps TOTAL_TIMESTEPS]
              [--torch-deterministic [TORCH_DETERMINISTIC]] [--cuda [CUDA]]
              [--track [TRACK]] [--wandb-project-name WANDB_PROJECT_NAME]
              [--wandb-entity WANDB_ENTITY] [--capture_video [CAPTURE_VIDEO]]
              [--num-envs NUM_ENVS] [--num-steps NUM_STEPS]
              [--anneal-lr [ANNEAL_LR]] [--gae [GAE]] [--gamma GAMMA]
              [--gae-lambda GAE_LAMBDA] [--num-minibatches NUM_MINIBATCHES]
              [--update-epochs UPDATE_EPOCHS] [--norm-adv [NORM_ADV]]
              [--clip-coef CLIP_COEF] [--clip-vloss [CLIP_VLOSS]]
              [--ent-coef ENT_COEF] [--vf-coef VF_COEF]
              [--max-grad-norm MAX_GRAD_NORM] [--target-kl TARGET_KL]

optional arguments:
  -h, --help            show this help message and exit
  --exp-name EXP_NAME   the name of this experiment
  --env-id ENV_ID       the id of the environment
  --learning-rate LEARNING_RATE
                        the learning rate of the optimizer
  --seed SEED           seed of the experiment
  --total-timesteps TOTAL_TIMESTEPS
                        total timesteps of the experiments
  --torch-deterministic [TORCH_DETERMINISTIC]
                        if toggled, `torch.backends.cudnn.deterministic=False`
  --cuda [CUDA]         if toggled, cuda will be enabled by default
  --track [TRACK]       if toggled, this experiment will be tracked with Weights
                        and Biases
  --wandb-project-name WANDB_PROJECT_NAME
                        the wandb's project name
  --wandb-entity WANDB_ENTITY
                        the entity (team) of wandb's project
  --capture_video [CAPTURE_VIDEO]
                        weather to capture videos of the agent performances (check
                        out `videos` folder)
  --num-envs NUM_ENVS   the number of parallel game environments
  --num-steps NUM_STEPS
                        the number of steps to run in each environment per policy
                        rollout
  --anneal-lr [ANNEAL_LR]
                        Toggle learning rate annealing for policy and value
                        networks
  --gae [GAE]           Use GAE for advantage computation
  --gamma GAMMA         the discount factor gamma
  --gae-lambda GAE_LAMBDA
                        the lambda for the general advantage estimation
  --num-minibatches NUM_MINIBATCHES
                        the number of mini-batches
  --update-epochs UPDATE_EPOCHS
                        the K epochs to update the policy
  --norm-adv [NORM_ADV]
                        Toggles advantages normalization
  --clip-coef CLIP_COEF
                        the surrogate clipping coefficient
  --clip-vloss [CLIP_VLOSS]
                        Toggles whether or not to use a clipped loss for the value
                        function, as per the paper.
  --ent-coef ENT_COEF   coefficient of the entropy
  --vf-coef VF_COEF     coefficient of the value function
  --max-grad-norm MAX_GRAD_NORM
                        the maximum norm for the gradient clipping
  --target-kl TARGET_KL
                        the target KL divergence threshold
```