an updated tutorial, with automatically filled pre_act_key and clear …

…measurement channel names PiperOrigin-RevId: 662859999 Change-Id: I79d3195c14ee37841e355e74d272d7407b130abe
google-deepmind · Aug 14, 2024 · 96f05d0 · 96f05d0
1 parent d6a228a
commit 96f05d0
Showing 1 changed file with 22 additions and 23 deletions.
diff --git a/examples/tutorials/agent_development.ipynb b/examples/tutorials/agent_development.ipynb
@@ -266,6 +266,7 @@
         "\n",
         "  def __init__(\n",
         "      self,\n",
+        "      agent_name:str,\n",
         "      **kwargs,\n",
         "  ):\n",
         "    #@markdown {agent_name} will be automatically replaced with the name of the specific agent\n",
@@ -276,7 +277,9 @@
         "    add_to_memory = True # @param {\"type\":\"boolean\"}\n",
         "    #@markdown If yes, the memory will start with this tag\n",
         "    memory_tag = '[self reflection]' # @param {\"type\":\"string\"}\n",
+        "    question_with_name = question.format(agent_name=agent_name)\n",
         "    super().__init__(\n",
+        "        pre_act_key=f'\\nQuestion: {question_with_name}\\nAnswer',\n",
         "        question=question,\n",
         "        answer_prefix=answer_prefix,\n",
         "        add_to_memory=add_to_memory,\n",
@@ -300,14 +303,17 @@
         "\n",
         "  def __init__(\n",
         "      self,\n",
+        "      agent_name:str,\n",
         "      **kwargs,\n",
         "  ):\n",
         "    question = 'Given the statements above, what kind of situation is {agent_name} in right now?' #@param {\"type\":\"string\"}\n",
         "    answer_prefix = '{agent_name} is currently ' #@param {\"type\":\"string\"}\n",
         "    add_to_memory = False # @param {\"type\":\"boolean\"}\n",
         "    memory_tag = '[situation reflection]' # @param {\"type\":\"string\"}\n",
+        "    question_with_name = question.format(agent_name=agent_name)\n",
         "\n",
         "    super().__init__(\n",
+        "        pre_act_key=f'\\nQuestion: {question_with_name}\\nAnswer',\n",
         "        question=question,\n",
         "        answer_prefix=answer_prefix,\n",
         "        add_to_memory=add_to_memory,\n",
@@ -331,12 +337,19 @@
         "class Question3(question_of_recent_memories.QuestionOfRecentMemories):\n",
         "  \"\"\"What would a person like the agent do in a situation like this?\"\"\"\n",
         "\n",
-        "  def __init__(self, **kwargs):\n",
+        "  def __init__(\n",
+        "      self,\n",
+        "      agent_name:str,\n",
+        "      **kwargs):\n",
         "    question = 'What would a person like {agent_name} do in a situation like this?' #@param {\"type\":\"string\"}\n",
         "    answer_prefix = '{agent_name} would ' #@param {\"type\":\"string\"}\n",
         "    add_to_memory = True # @param {\"type\":\"boolean\"}\n",
         "    memory_tag = '[intent reflection]' # @param {\"type\":\"string\"}\n",
+        "\n",
+        "    question_with_name = question.format(agent_name=agent_name)\n",
+        "\n",
         "    super().__init__(\n",
+        "        pre_act_key=f'\\nQuestion: {question_with_name}\\nAnswer',\n",
         "        question=question,\n",
         "        answer_prefix=answer_prefix,\n",
         "        add_to_memory=add_to_memory,\n",
@@ -362,33 +375,23 @@
         "    model: language_model.LanguageModel,\n",
         "    clock: game_clock.MultiIntervalClock,\n",
         ") -\u003e Sequence[question_of_recent_memories.QuestionOfRecentMemories]:\n",
-        "  question_1_label = (\n",
-        "      f'\\nQuestion: What kind of person is {agent_name}?\\nAnswer'\n",
-        "  )\n",
+        "\n",
         "  question_1 = Question1(\n",
+        "      agent_name=agent_name,\n",
         "      model=model,\n",
-        "      pre_act_key=question_1_label,\n",
-        "      logging_channel=measurements.get_channel('SelfPerception').on_next,\n",
-        "  )\n",
-        "  question_2_label = (\n",
-        "      f'\\nQuestion: What kind of situation is {agent_name} in '\n",
-        "      'right now?\\nAnswer'\n",
+        "      logging_channel=measurements.get_channel('Question_1').on_next,\n",
         "  )\n",
         "  question_2 = Question2(\n",
+        "      agent_name=agent_name,\n",
         "      model=model,\n",
         "      clock_now=clock.now,\n",
-        "      pre_act_key=question_2_label,\n",
-        "      logging_channel=measurements.get_channel('SituationPerception').on_next,\n",
-        "  )\n",
-        "  question_3_label = (\n",
-        "      f'\\nQuestion: What would a person like {agent_name} do in '\n",
-        "      'a situation like this?\\nAnswer'\n",
+        "      logging_channel=measurements.get_channel('Question_2').on_next,\n",
         "  )\n",
         "  question_3 = Question3(\n",
+        "      agent_name=agent_name,\n",
         "      model=model,\n",
         "      clock_now=clock.now,\n",
-        "      pre_act_key=question_3_label,\n",
-        "      logging_channel=measurements.get_channel('PersonBySituation').on_next,\n",
+        "      logging_channel=measurements.get_channel('Question_3').on_next,\n",
         "  )\n",
         "\n",
         "  return (question_1, question_2, question_3)"
@@ -489,7 +492,7 @@
         "\n",
         "\n",
         "  question_components = _make_question_components(\n",
-        "      agent_name=agent_name, model=model, clock=clock, measurements=measurements\n",
+        "      model=model, clock=clock, measurements=measurements\n",
         "  )\n",
         "\n",
         "  core_components = (\n",
@@ -662,10 +665,6 @@
   ],
   "metadata": {
     "colab": {
-      "last_runtime": {
-        "build_target": "",
-        "kind": "private"
-      },
       "private_outputs": true,
       "provenance": [],
       "toc_visible": true