danielpcox/DemoPPOPong.ipynb

## DemoPPOPong.ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%load_ext autoreload\n%autoreload 2\n%matplotlib inline\n\nframes = 10000\nseed = 42\n\nfrom exp.nb_PPOPong import make_env, rotate_framestack, framestack, actor_critic, optimizer, load\n\nimport matplotlib.pyplot as plt\nfrom IPython.display import clear_output\n\nenv = make_env('PongNoFrameskip-v4', seed, 0)()\n\nload(actor_critic, optimizer, load_file='PPOPong-10M.pt')\nactor_critic.eval()\n\nobs = env.reset()\nrotate_framestack(obs)\ndone = False\nwhile not done:\n    _,actions,_ = actor_critic.act(framestack, deterministic=True)\n    cpu_actions = actions.squeeze(1).cpu().numpy()\n    obs,reward,done,info = env.step(cpu_actions[0])\n    rotate_framestack(obs)\n    clear_output(wait=True)\n    plt.imshow(obs.squeeze(0))\n    plt.show()",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "gist": {
      "id": "",
      "data": {
        "description": "danielpcox/notebooks/RL/DemoModel.ipynb",
        "public": true
      }
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.8",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}
	{
	"cells": [
	{
	"metadata": {
	"trusted": true
	},
	"cell_type": "code",
	"source": "%load_ext autoreload\n%autoreload 2\n%matplotlib inline\n\nframes = 10000\nseed = 42\n\nfrom exp.nb_PPOPong import make_env, rotate_framestack, framestack, actor_critic, optimizer, load\n\nimport matplotlib.pyplot as plt\nfrom IPython.display import clear_output\n\nenv = make_env('PongNoFrameskip-v4', seed, 0)()\n\nload(actor_critic, optimizer, load_file='PPOPong-10M.pt')\nactor_critic.eval()\n\nobs = env.reset()\nrotate_framestack(obs)\ndone = False\nwhile not done:\n _,actions,_ = actor_critic.act(framestack, deterministic=True)\n cpu_actions = actions.squeeze(1).cpu().numpy()\n obs,reward,done,info = env.step(cpu_actions[0])\n rotate_framestack(obs)\n clear_output(wait=True)\n plt.imshow(obs.squeeze(0))\n plt.show()",
	"execution_count": 1,
	"outputs": [
	{
	"output_type": "display_data",
	"data": {
	"text/plain": "<Figure size 432x288 with 1 Axes>",
	"image/png": 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\n"
	},
	"metadata": {}
	}
	]
	},
	{
	"metadata": {
	"trusted": true
	},
	"cell_type": "code",
	"source": "",
	"execution_count": null,
	"outputs": []
	}
	],
	"metadata": {
	"gist": {
	"id": "",
	"data": {
	"description": "danielpcox/notebooks/RL/DemoModel.ipynb",
	"public": true
	}
	},
	"kernelspec": {
	"name": "python3",
	"display_name": "Python 3",
	"language": "python"
	},
	"language_info": {
	"name": "python",
	"version": "3.6.8",
	"mimetype": "text/x-python",
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"pygments_lexer": "ipython3",
	"nbconvert_exporter": "python",
	"file_extension": ".py"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}