"This notebook builds on the concepts in the [argument generation](How_to_call_functions_with_chat_models.ipynb) notebook, by creating an agent with access to a knowledge base and two functions that it can call based on the user requirement.\n",
"We'll create an agent that uses data from arXiv to answer questions about academic subjects. It has two functions at its disposal:\n",
"- **get_articles**: A function that gets arXiv articles on a subject and summarizes them for the user with links.\n",
"- **read_article_and_summarize**: This function takes one of the previously searched articles, reads it in its entirety and summarizes the core argument, evidence and conclusions.\n",
"\n",
"This will get you comfortable with a multi-function workflow that can choose from multiple services, and where some of the data from the first function is persisted to be used by the second.\n",
"\n",
"## Walkthrough\n",
"\n",
"This cookbook takes you through the following workflow:\n",
"\n",
"- **Search utilities:** Creating the two functions that access arXiv for answers.\n",
"- **Configure Agent:** Building up the Agent behaviour that will assess the need for a function and, if one is required, call that function and present results back to the agent.\n",
"- **arXiv conversation:** Put all of this together in live conversation.\n"
"We'll first set up some utilities that will underpin our two functions.\n",
"\n",
"Downloaded papers will be stored in a directory (we use ```./data/papers``` here). We create a file ```arxiv_library.csv``` to store the embeddings and details for downloaded papers to retrieve against using ```summarize_text```."
"{'title': 'Proximal Policy Optimization and its Dynamic Version for Sequence Generation',\n",
" 'summary': 'In sequence generation task, many works use policy gradient for model\\noptimization to tackle the intractable backpropagation issue when maximizing\\nthe non-differentiable evaluation metrics or fooling the discriminator in\\nadversarial learning. In this paper, we replace policy gradient with proximal\\npolicy optimization (PPO), which is a proved more efficient reinforcement\\nlearning algorithm, and propose a dynamic approach for PPO (PPO-dynamic). We\\ndemonstrate the efficacy of PPO and PPO-dynamic on conditional sequence\\ngeneration tasks including synthetic experiment and chit-chat chatbot. The\\nresults show that PPO and PPO-dynamic can beat policy gradient by stability and\\nperformance.',\n",
"- The paper argues that Proximal Policy Optimization (PPO) and its dynamic variant (PPO-dynamic) significantly improve sequence generation tasks, particularly for chit-chat chatbots, by addressing the instability and suboptimal performance associated with traditional policy gradient methods.\n",
"\n",
"### Evidence\n",
"- **Challenges with Traditional Methods**: Traditional policy gradient methods, like REINFORCE, suffer from unstable training and poor performance due to large updates and similar action tendencies, especially in non-differentiable evaluation contexts (e.g., BLEU scores).\n",
"- **PPO Advantages**: PPO regularizes policy updates, enhancing training stability and enabling the generation of coherent and diverse chatbot responses.\n",
"- **Dynamic PPO Approach**: PPO-dynamic introduces adaptive constraints on KL-divergence, allowing for dynamic adjustments based on action probabilities, which leads to improved training performance.\n",
"- **Experimental Validation**: The authors conducted experiments on synthetic counting tasks and real-world chit-chat scenarios, demonstrating that PPO and PPO-dynamic outperform traditional methods like REINFORCE and SeqGAN in terms of stability and performance metrics (e.g., BLEU-2 scores).\n",
"- **Results**: PPO-dynamic showed faster convergence and higher precision in the counting task, and it achieved the best performance in the chit-chat task, indicating its effectiveness in generating diverse and contextually appropriate responses.\n",
"\n",
"### Conclusions\n",
"- The introduction of PPO and PPO-dynamic enhances the training stability and output diversity in sequence generation tasks, making them more suitable for applications like chatbots.\n",
"- The dynamic variant of PPO not only improves performance but also accelerates convergence, addressing the limitations of traditional policy gradient methods and providing a robust framework for reinforcement learning in sequence generation."
"We'll create our agent in this step, including a ```Conversation``` class to support multiple turns with the API, and some Python functions to enable interaction between the ```ChatCompletion``` API and our knowledge base functions."
"Here are some recent papers that discuss Proximal Policy Optimization (PPO) in reinforcement learning, explaining its mechanics and various enhancements:\n",
"\n",
"1. **[Proximal Policy Optimization and its Dynamic Version for Sequence Generation](http://arxiv.org/abs/1808.07982v1)** \n",
" - *Summary:* This paper applies PPO to sequence generation tasks, demonstrating that it outperforms traditional policy gradient methods in terms of stability and performance. It introduces a dynamic version of PPO for these tasks.\n",
" - [PDF](http://arxiv.org/pdf/1808.07982v1)\n",
"\n",
"2. **[CIM-PPO: Proximal Policy Optimization with Liu-Correntropy Induced Metric](http://arxiv.org/abs/2110.10522v3)** \n",
" - *Summary:* This work investigates the asymmetry in KL divergence in PPO-KL and proposes PPO-CIM as an enhanced version with lower computation costs and improved policy updates, validated through experiments on continuous-action tasks.\n",
" - [PDF](http://arxiv.org/pdf/2110.10522v3)\n",
"\n",
"3. **[A2C is a special case of PPO](http://arxiv.org/abs/2205.09123v1)** \n",
" - *Summary:* This paper shows that A2C can be viewed as a special case of PPO, providing theoretical justifications and empirical evidence demonstrating their equivalence under controlled conditions.\n",
"4. **[Proximal Policy Optimization via Enhanced Exploration Efficiency](http://arxiv.org/abs/2011.05525v1)** \n",
" - *Summary:* This paper enhances the PPO algorithm by improving exploration strategies, proposing IEM-PPO, which shows better sample efficiency and rewards than standard methods in complex environments.\n",
"5. **[ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models](http://arxiv.org/abs/2310.10505v4)** \n",
" - *Summary:* The ReMax method is proposed as an alternative to PPO for training large language models, reducing hyper-parameter tuning complexities and enhancing training efficiency.\n",
"6. **[Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks](http://arxiv.org/abs/2310.17805v1)** \n",
" - *Summary:* This work examines the applicability of DreamerV3's tricks to PPO, revealing mixed outcomes and providing insights into the clipping mechanism in PPO's performance.\n",
"7. **[Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective](http://arxiv.org/abs/2110.13799v4)** \n",
" - *Summary:* This paper establishes a theoretical grounding for PPO-Clip and introduces new interpretive frameworks for its mechanics, showing improved convergence properties.\n",
"8. **[Colored Noise in PPO: Improved Exploration and Performance through Correlated Action Sampling](http://dx.doi.org/10.1609/aaai.v38i11.29139)** \n",
" - *Summary:* This study proposes a variant of PPO using correlated noise for improved exploration, demonstrating enhanced performance over traditional approaches.\n",
"9. **[A dynamical clipping approach with task feedback for Proximal Policy Optimization](http://arxiv.org/abs/2312.07624v3)** \n",
" - *Summary:* The paper presents Pb-PPO, which dynamically adjusts the clipping bounds in PPO to enhance returns, showing improved performance across various tasks.\n",
" - [PDF](http://arxiv.org/pdf/2312.07624v3)\n",
"\n",
"10. **[PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration](http://arxiv.org/abs/2212.06343v1)** \n",
" - *Summary:* Introducing PPO-UE, which incorporates uncertainty-aware exploration, this paper shows improvements in convergence speed and performance compared to standard PPO.\n",
" - [PDF](http://arxiv.org/pdf/2212.06343v1)\n",
"\n",
"These papers provide a comprehensive view of the developments and enhancements in PPO and how it operates within the reinforcement learning framework. You can click on the titles to access the full articles."
"- The paper argues for the adoption of Proximal Policy Optimization (PPO) and its dynamic variant (PPO-dynamic) as superior methods for sequence generation tasks, particularly in the context of chit-chat chatbots, compared to traditional policy gradient methods.\n",
"- It highlights the instability and suboptimal performance of traditional policy gradient methods, such as REINFORCE, and presents PPO as a more stable and efficient alternative.\n",
"- **Challenges with Policy Gradient**: Traditional methods lead to unstable training and poor performance due to large updates and similar action tendencies, especially in non-differentiable evaluation metrics like BLEU scores.\n",
"- **PPO Advantages**: PPO regularizes policy updates, enhancing stability and coherence in chatbot responses.\n",
"- **Dynamic PPO Approach**: PPO-dynamic introduces dynamic adjustments to the KL-divergence bounds, allowing for more flexible and effective training.\n",
"- **Experimental Validation**: Experiments on synthetic tasks and real-world chit-chat scenarios demonstrate that PPO and PPO-dynamic outperform REINFORCE and other algorithms (like MIXER and SeqGAN) in terms of stability and performance metrics, including BLEU-2 scores.\n",
"- **Results**: PPO-dynamic showed significant improvements in precision on counting tasks and achieved the highest BLEU-2 score for chatbot responses, indicating better performance in generating diverse and accurate outputs.\n",
"- The paper concludes that replacing traditional policy gradient methods with PPO, particularly the dynamic version, leads to more stable training and faster convergence in sequence generation tasks.\n",
"- The proposed PPO-dynamic method enhances the training process by dynamically adjusting constraints, resulting in improved performance and efficiency in generating human-like conversational agents.\n",
"- Future research directions are suggested to further explore the potential of PPO and its adaptations in natural language processing applications."
