Fix figure name and clarify a couple code examples
Aurélien Geron
parent
c5f4b41cf5
commit
3ef350ab4c
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@ -233,7 +233,7 @@
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"$ xvfb-run -s \"-screen 0 1400x900x24\" jupyter notebook\n",
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"```\n",
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"\n",
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"Alternatively, you can install the `pyvirtualdisplay` Python library which wraps Xvfb:\n",
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"Alternatively, you can install the [pyvirtualdisplay](https://github.com/ponty/pyvirtualdisplay) Python library which wraps Xvfb:\n",
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"\n",
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"```bash\n",
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"python3 -m pip install -U pyvirtualdisplay\n",
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@ -1319,8 +1319,8 @@
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" indices = np.random.randint(len(replay_memory), size=batch_size)\n",
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" batch = [replay_memory[index] for index in indices]\n",
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" states, actions, rewards, next_states, dones = [\n",
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" np.array([experience[index] for experience in batch])\n",
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" for index in range(5)]\n",
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" np.array([experience[field_index] for experience in batch])\n",
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" for field_index in range(5)]\n",
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" return states, actions, rewards, next_states, dones"
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]
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},
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@ -1580,7 +1580,7 @@
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"plt.plot(rewards)\n",
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"plt.xlabel(\"Episode\", fontsize=14)\n",
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"plt.ylabel(\"Sum of rewards\", fontsize=14)\n",
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"save_fig(\"dqn_rewards_plot\")\n",
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"save_fig(\"double_dqn_rewards_plot\")\n",
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"plt.show()"
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]
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},
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@ -2057,7 +2057,7 @@
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" current_frame_delta = np.maximum(obs[..., 3] - obs[..., :3].mean(axis=-1), 0.)\n",
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" img[..., 0] += current_frame_delta\n",
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" img[..., 2] += current_frame_delta\n",
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" img = (img - img.min()) / (img.max() - img.min())\n",
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" img = np.clip(img / 150, 0, 1)\n",
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" plt.imshow(img)\n",
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" plt.axis(\"off\")"
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]
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@ -2459,58 +2459,6 @@
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"train_agent(n_iterations=200) # change this to 10 million or more!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 119,
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"metadata": {},
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"outputs": [],
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"source": [
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"num_eval_episodes = 10\n",
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"eval_metrics = [\n",
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" tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),\n",
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" tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes),\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 120,
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"metadata": {},
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"outputs": [],
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"source": [
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"eval_tf_env = suite_atari.load(\n",
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" environment_name,\n",
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" max_episode_steps=max_episode_steps,\n",
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" gym_env_wrappers=[AtariPreprocessing, FrameStack4])\n",
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"\n",
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"eval_tf_env = TFPyEnvironment(eval_tf_env)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 121,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tf_agents.eval import metric_utils\n",
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"\n",
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"results = metric_utils.eager_compute(\n",
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" eval_metrics,\n",
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" eval_tf_env,\n",
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" agent.policy,\n",
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" num_episodes=num_eval_episodes,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 122,
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"metadata": {},
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"outputs": [],
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"source": [
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"results"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -2534,7 +2482,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 123,
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"execution_count": 119,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -2548,7 +2496,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 124,
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"execution_count": 120,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -2557,7 +2505,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 125,
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"execution_count": 121,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -2573,7 +2521,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 126,
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"execution_count": 122,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -2596,7 +2544,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 127,
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"execution_count": 123,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -2608,7 +2556,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 128,
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"execution_count": 124,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -2617,7 +2565,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 129,
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"execution_count": 125,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -2640,7 +2588,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 130,
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"execution_count": 126,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -2689,7 +2637,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 131,
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"execution_count": 127,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -2700,7 +2648,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 132,
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"execution_count": 128,
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"metadata": {},
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"outputs": [],
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"source": [
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