process stops progressing after reaching "generating idea 2/2

[clean-e2map]

Is the process stopping because I requested only 2 ideas to be generated?

I'm also curious about how to obtain the full paper.

I've been waiting for an hour, and the GPT API usage has been stuck at $0.5. Additionally, the server isn't utilizing any GPU resources.

Below is all the output I received in the terminal after running the command. Please refer to it.

(ai_scientist) root@ubuntu:~/AI_Scientist/AI-Scientist$ python launch_scientist.py --model "gpt-4o-2024-05-13" --experiment nanoGPT_lite --num-ideas 2 Using GPUs: [0, 1, 2] Using OpenAI API with model gpt-4o-2024-05-13.

Generating idea 1/2 Iteration 1/3 {'Name': 'heterogeneous_learning_strategies', 'Title': 'Exploring Heterogeneous Learning Strategies in Multi-Agent Systems', 'Experiment': 'This experiment will involve modifying the training loop to support different learning strategies for individual agents within a multi-agent system. We will implement variations such as different exploration-exploitation balances, distinct learning rates, and alternative reward structures. The performance of these heterogeneous systems will be compared against baseline systems with homogeneous learning strategies. Key metrics will include collective performance, convergence speed, and robustness to varying tasks.', 'Interestingness': 8, 'Feasibility': 7, 'Novelty': 8} Iteration 2/3 {'Name': 'heterogeneous_learning_strategies', 'Title': 'Exploring Heterogeneous Learning Strategies in Multi-Agent Systems', 'Experiment': 'This experiment will involve modifying the training loop to support different learning strategies for individual agents within a multi-agent system. Specifically, we will implement variations such as different exploration-exploitation balances (e.g., epsilon-greedy vs. softmax), distinct learning rates, and alternative reward structures. The performance of these heterogeneous systems will be compared against baseline systems with homogeneous learning strategies using metrics like average reward, convergence time, and robustness to varying tasks. Key functions to modify include the training loop and the agent initialization process to support multiple strategies.', 'Interestingness': 8, 'Feasibility': 7, 'Novelty': 8} Iteration 3/3 {'Name': 'heterogeneous_learning_strategies', 'Title': 'Exploring Heterogeneous Learning Strategies in Multi-Agent Systems', 'Experiment': 'This experiment will involve modifying the training loop to support different learning strategies for individual agents within a multi-agent system. Specifically, we will implement variations such as different exploration-exploitation balances (e.g., epsilon-greedy vs. softmax), distinct learning rates, and alternative reward structures. The performance of these heterogeneous systems will be compared against baseline systems with homogeneous learning strategies using metrics like average reward, convergence time, and robustness to varying tasks. Key functions to modify include the training loop and the agent initialization process to support multiple strategies. Multiple experimental runs will be conducted to account for variability in learning outcomes, and detailed documentation will be provided to ensure reproducibility of the results.', 'Interestingness': 8, 'Feasibility': 7, 'Novelty': 8} Idea generation converged after 3 iterations.

Generating idea 2/2 Iteration 1/3 {'Name': 'communication_strategies', 'Title': 'Impact of Communication Strategies on Multi-Agent System Performance', 'Experiment': 'This experiment will involve modifying the training loop to include communication phases where agents can share information. We will implement different communication strategies such as synchronous vs. asynchronous communication, and centralized vs. decentralized communication. The performance of these strategies will be evaluated using metrics like convergence rate, final performance, and robustness to varying tasks. Key functions to modify include the training loop to add communication phases and the agent initialization to support different communication protocols. Multiple experimental runs will be conducted to account for variability in learning outcomes, and detailed documentation will be provided to ensure reproducibility of the results.', 'Interestingness': 8, 'Feasibility': 7, 'Novelty': 8} Iteration 2/3 {'Name': 'communication_strategies', 'Title': 'Impact of Communication Strategies on Multi-Agent System Performance', 'Experiment': 'This experiment will involve modifying the training loop to include communication phases where agents can share information. We will implement different communication strategies such as: (1) Synchronous communication where agents share information at fixed intervals. (2) Asynchronous communication where agents share information independently. (3) Centralized communication where a central node aggregates and redistributes information. (4) Decentralized communication where agents communicate directly with each other. The performance of these strategies will be evaluated using metrics like convergence rate, final performance, and robustness to varying tasks. Key functions to modify include the training loop to add communication phases and the agent initialization to support different communication protocols. Performance metrics will be collected during and after training and analyzed to determine the effectiveness of each communication strategy. Multiple experimental runs will be conducted to account for variability in learning outcomes, and detailed documentation will be provided to ensure reproducibility of the results.', 'Interestingness': 8, 'Feasibility': 7, 'Novelty': 8} Idea generation converged after 2 iterations.

Checking novelty of idea 0: Fundamental Principle Response Status Code: 200 Response Content: {"total": 538, "offset": 0, "next": 10, "data": [{"paperId": "09498b36bb66169f05e6be614a5493576123b6ed", "title": "Large Language Models to the Rescue: Deadlock Resolution in Multi-Robot Systems", "abstract": "Multi-agent robotic systems are prone to deadlocks in an obstacle environment where the system can get stuck away from its desired location under a smooth low-level control policy. Without an external intervention, often in terms of a high-level command, it is not possible to guarantee tha Response Status Code: 429 Response Content: {"message": "Too Many Requests. Please wait and try again or apply for a key for higher rate limits. https://www.semanticscholar.org/product/api#api-key-form", "code": "429"} Backing off 0.9 seconds after 1 tries calling function search_for_papers at 00:34:35 Response Status Code: 200 Response Content: {"total": 147, "offset": 0, "next": 10, "data": [{"paperId": "bf8d94a808bcd41062d1d61bab9045d61c610241", "title": "Co-Evolving Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation", "abstract": "Multi-Agent Reinforcement Learning (MARL) is extensively utilized for addressing intricate tasks that involve cooperation and competition among agents in Multi-Agent Systems (MAS). However, learning such tasks from scratch is challenging and often unfeasible, especially for Response Status Code: 200 Response Content: {"total": 1079, "offset": 0, "next": 10, "data": [{"paperId": "a4391af54bea44c52956822fa83d1fc8ed6f8251", "title": "Multi-Agent Deep Reinforcement Learning Based Transmission Latency Minimization for Delay-Sensitive Cognitive Satellite-UAV Networks", "abstract": "With the ubiquitous deployment of a massive number of Internet-of-Things (IoT) devices, the satellite-aerial networks are becoming a promising candidate to provide flexible and seamless service for IoT applications. Concerning about the Response Status Code: 200 Response Content: {"total": 153, "offset": 0, "next": 10, "data": [{"paperId": "0b41a6899c29a04e1217e6cc80a3d915ea18e2d8", "title": "FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making", "abstract": "Large language models (LLMs) have demonstrated notable potential in conducting complex tasks and are increasingly utilized in various financial applications. However, high-quality sequential financial investment decision-making remains challenging.

[conglu1997]

Keep it running for it start experiments. It will run all ideas that are deemed novel. It could be the case that the two initial ideas are not novel.

And yes, only 2 ideas will be generated if you specify 2 ideas!

[clean-e2map]

Keep it running for it start experiments. It will run all ideas that are deemed novel. It could be the case that the two initial ideas are not novel.

And yes, only 2 ideas will be generated if you specify 2 ideas!

Thank you for reply directly!! As you mentioned, when I force-stopped the process using ctrl+c, I encountered the following error message. It seems that the GPT-4o limit is 30,000 TPM, but I can't control this value; I can only manage the usage limit! The GPT-4o-mini model has a limit of 200,000 TPM, so I tried running it with that model instead. but I meet under error sentence.

openai.RateLimitError: Error code: 429 - {'error': {'message': 'Request too large for gpt-4o in organization org-v70GRNvNZutI6mTdyL5gd2Qu on tokens per min (TPM): Limit 30000, Requested 30052. The input or output tokens must be reduced in order to run successfully. Visit https://platform.openai.com/account/rate-limits to learn more.', 'type': 'tokens', 'param': None, 'code': 'rate_limit_exceeded'}}

launch_scientist.py: error: argument --model: invalid choice: 'gpt-4o-mini' (choose from 'claude-3-5-sonnet-20240620', 'gpt-4o-2024-05-13', 'deepseek-coder-v2-0724', 'llama3.1-405b', 'bedrock/anthropic.claude-3-sonnet-20240229-v1:0', 'bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0', 'bedrock/anthropic.claude-3-haiku-20240307-v1:0', 'bedrock/anthropic.claude-3-opus-20240229-v1:0vertex_ai/claude-3-opus@20240229', 'vertex_ai/claude-3-5-sonnet@20240620', 'vertex_ai/claude-3-sonnet@20240229', 'vertex_ai/claude-3-haiku@20240307')

[conglu1997]

We would advise sticking to GPT-4o but trying to increase your OpenAI account tier.