Write a Python function that uses the A* search algorithm to plan actions for a team of 3 agents, each with its own set of possible actions, given the current state of a dynamic environment and the goal state, assuming the environment is represented as a graph and the actions are represented as transitions between nodes in the graph.
AGENT WORKER: upload output to cloud storage as CSV file with task_id and timestamp
AGENT WORKER: retrieve next task from task_queue and process input data using pre-trained model
AGENT REVIEWER: verify_result result from worker_id workers.csv and assign score in JSON format
Can you provide an evaluation of the effectiveness of recent meta-learning approaches for multi-agent planning, including reinforcement learning and model-based methods, with specific focus on their ability to handle uncertain environments?
What is the impact of incorporating game theory and learning objectives into multi-agent planning algorithms, with a focus on applications in robotics and supply chain management?
What are the limitations and potential avenues for improvement in current state-of-the-art multi-agent planning systems, specifically examining the trade-offs between individual agent autonomy and centralized control?