From d45341397171eb9c3f2ae2d8e8b7509839bfefbf Mon Sep 17 00:00:00 2001 From: Berbardo Date: Thu, 25 Nov 2021 22:27:47 -0300 Subject: [PATCH 1/2] :pencil: Adiciona tarefa extra --- README.md | 8 ++++++++ img/tarefa_extra.png | Bin 0 -> 212147 bytes 2 files changed, 8 insertions(+) create mode 100644 img/tarefa_extra.png diff --git a/README.md b/README.md index 235cc9e..a36ac6b 100644 --- a/README.md +++ b/README.md @@ -21,3 +21,11 @@ Na segunda parte do projeto, você deverá implementar e comparar diferentes alg Este repositório já contém um tutorial simples de como utilizar a biblioteca, que você pode conferir **[aqui](Tutorial%20-%20Stable%20Baselines.ipynb)**! Para começar a tarefa, basta acessar e alterar **[este notebook](Tarefa%202%20-%20Stable%20Baselines.ipynb)**! + + + +Por fim, propomos uma tarefa extra 100% opcional caso queira treinar suas habilidades de RL! Ao final do projeto Trainee, caso seja interessante, você pode optar por treinar um novo modelo para o ambiente **slime-volleyball**, um jogo de vôlei no qual seu agente pode competir com os jogadores de outros membros. + +Para treinar seu próprio agente, basta apenas rodar [este Jupyter Notebook](https://colab.research.google.com/drive/1hzRhoJ8UuBs-rK8DID3l85k-8Z8MxqXL#forceEdit=true&sandboxMode=true) em seu Google Colaboratory. Recomendamos, antes de começar, conversar com algum dos veteranos da área, copiar o notebook para seu drive e editar os hiperparâmetros conforme necessário! + +**DISCLAMER:** Os agentes de vôlei demorarão muitas horas (3+) para serem treinados e nem sempre superarão a IA já programada no próprio jogo, então não se desanime caso não esteja obtendo resultados rápidos! 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edade732dd6dc16be93aa19692bde05f8150b2ae Mon Sep 17 00:00:00 2001 From: Berbardo Date: Sun, 5 Dec 2021 13:20:08 -0300 Subject: [PATCH 2/2] :recycle: Adiciona notebook de slime volleyball ao GitHub --- README.md | 6 +- Tarefa Extra - SlimeVolleyball.ipynb | 124 +++++++++++++++++++++++++++ 2 files changed, 127 insertions(+), 3 deletions(-) create mode 100644 Tarefa Extra - SlimeVolleyball.ipynb diff --git a/README.md b/README.md index a36ac6b..e6d16f2 100644 --- a/README.md +++ b/README.md @@ -24,8 +24,8 @@ Para começar a tarefa, basta acessar e alterar **[este notebook](Tarefa%202%20- -Por fim, propomos uma tarefa extra 100% opcional caso queira treinar suas habilidades de RL! Ao final do projeto Trainee, caso seja interessante, você pode optar por treinar um novo modelo para o ambiente **slime-volleyball**, um jogo de vôlei no qual seu agente pode competir com os jogadores de outros membros. +Por fim, propomos uma tarefa extra 100% opcional caso queira treinar suas habilidades de RL! Ao final do projeto Trainee, caso ache interessante, você pode optar por treinar um novo modelo para o ambiente **slime-volleyball**, um jogo de vôlei no qual seu agente pode competir com os jogadores de outros membros. -Para treinar seu próprio agente, basta apenas rodar [este Jupyter Notebook](https://colab.research.google.com/drive/1hzRhoJ8UuBs-rK8DID3l85k-8Z8MxqXL#forceEdit=true&sandboxMode=true) em seu Google Colaboratory. Recomendamos, antes de começar, conversar com algum dos veteranos da área, copiar o notebook para seu drive e editar os hiperparâmetros conforme necessário! +Para treinar seu próprio agente, basta rodar [este Jupyter Notebook](https://colab.research.google.com/github/turing-usp/Trainee-RL-2021/blob/main/Tarefa%20Extra%20-%20SlimeVolleyball.ipynb) em seu Google Colaboratory. Recomendamos, antes de começar, que você converse com algum dos veteranos da área, copie o notebook para seu drive e edite os hiperparâmetros conforme necessário! -**DISCLAMER:** Os agentes de vôlei demorarão muitas horas (3+) para serem treinados e nem sempre superarão a IA já programada no próprio jogo, então não se desanime caso não esteja obtendo resultados rápidos! Lembrando que esta é uma tarefa **100% opcional**. \ No newline at end of file +**DISCLAMER:** Os agentes de vôlei demorarão muitas horas (3+) para serem treinados e nem sempre superarão a IA já programada no próprio jogo, então não se desanime caso não esteja obtendo resultados rapidamente! Lembrando que esta é uma tarefa **100% opcional**. \ No newline at end of file diff --git a/Tarefa Extra - SlimeVolleyball.ipynb b/Tarefa Extra - SlimeVolleyball.ipynb new file mode 100644 index 0000000..c5ea2f7 --- /dev/null +++ b/Tarefa Extra - SlimeVolleyball.ipynb @@ -0,0 +1,124 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "SlimeVolleyball.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "VYIynIatFL8y" + }, + "source": [ + "# Treinando seu modelo de vôlei\n", + "\n", + "![](https://camo.githubusercontent.com/7eba6ff826871a5f1b9fb48d5dc7472dbf6bdbcda80bfb975bdde5a0ff71fdf5/68747470733a2f2f6f746f726f2e6e65742f696d672f736c696d6567796d2f706978656c2e676966)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "eO3Jc2pOXLgD" + }, + "source": [ + "# Instala as bibliotecas necessárias\n", + "!pip install stable_baselines3\n", + "!pip install git+https://github.com/turing-usp/slimevolleygym" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TibOzPxrFRKB" + }, + "source": [ + "Vamos salvar os modelos treinados no seu drive: 😈😈😈" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xf0FPkw85huS" + }, + "source": [ + "from google.colab import drive\n", + "\n", + "# Permite acesso ao seu drive\n", + "drive.mount('/content/drive')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ucaK_ds0XSh6" + }, + "source": [ + "import os\n", + "import gym\n", + "import slimevolleygym\n", + "from slimevolleygym import SurvivalRewardEnv\n", + "from torch import nn\n", + "\n", + "from stable_baselines3.ppo import PPO\n", + "from stable_baselines3.common.callbacks import EvalCallback\n", + "\n", + "# Parâmetros para o treinamento\n", + "SEED = 721 # Seed\n", + "NUM_TIMESTEPS = int(2e7) # Número de timesteps de treino\n", + "EVAL_FREQ = 100000 # A cada quantos timesteps o modelo é avaliado (ver EvalCallback)\n", + "EVAL_EPISODES = 100 # Por quantos episódios o modelo é avaliado (ver EvalCallback)\n", + "LOGDIR = \"/content/drive/MyDrive/ppo\" # Local de salvar o modelo (no seu drive)\n", + "\n", + "# Cria o ambiente\n", + "env = gym.make(\"SlimeVolley-v0\")\n", + "env.seed(SEED)\n", + "\n", + "# Arquitetura do modelo de PPO (usar caso somente tenha interesse em alterar o padrão de 64x64 neurônios)\n", + "model_arch=dict(\n", + " log_std_init=-2,\n", + " ortho_init=False,\n", + " activation_fn=nn.ReLU,\n", + " net_arch=[dict(pi=[64, 64], vf=[64, 64])] # possível trocar para [128, 128] e outros do tipo\n", + " )\n", + "\n", + "# Cria o modelo (mudar hiperparâmetros a vontade)\n", + "model = PPO('MlpPolicy', env, n_steps=4096, batch_size=32, ent_coef=0.005, n_epochs=10,\n", + " learning_rate=3e-4, clip_range=0.2, gamma=0.99, gae_lambda=0.95, verbose=2\n", + " # , policy_kwargs = model_arch # Descomentar caso tenha interesse em usar outras arquiteturas!!!\n", + " )\n", + "\n", + "# Carrega modelo salvo caso já exista\n", + "if os.path.exists(LOGDIR + \"/best_model\"):\n", + " model = PPO.load(LOGDIR + \"/best_model\", env=env, n_steps=4096, n_epochs=10)\n", + "\n", + "# Salva o melhor modelo a cada avaliação\n", + "eval_callback = EvalCallback(env, best_model_save_path=LOGDIR, log_path=LOGDIR, eval_freq=EVAL_FREQ, n_eval_episodes=EVAL_EPISODES)\n", + "\n", + "# Treinamento do Modelo\n", + "model.learn(total_timesteps=NUM_TIMESTEPS, callback=eval_callback)\n", + "\n", + "model.save(os.path.join(LOGDIR, \"final_model\"))\n", + "\n", + "env.close()" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file