# Simulate for i iterations i = initial_i = 10000 print(f"Iterations remaining {i}") iterations_list = [] agent_iterations = 0 while True: agent_a.iterate_rl() agent_iterations += 1 # Explore until goal is found, then try again if agent_a.at_goal(): i -= 1 # Train until done if i == 0: break # Reset agent in when retraining iterations_list.append(agent_iterations) agent_iterations = 0 agent_a.reset() agent_a.set_state(init_agent_a) print(f"Iterations remaining {i}") print(f"Iterations over time: {iterations_list}")
# Migration step, for a star network for agent in agents: # Perform migration(s) for migration_target in migration_map[agent]: migration = agent.emigrate(0.01) migration_target.immigrate(migration) # Iterate the agents, which now includes the migrated population. for agent in agents: agent.iterate_rl() agent_iterations += 1 # Explore until C finds goal, then try again if agent_c.at_goal(): i -= 1 # Train until done if i == 0: break # Reset agent in when retraining iterations_list.append(agent_iterations) agent_iterations = 0 for agent in agents: agent.reset() agent.set_state(init_state_map[agent]) print(f"Iterations remaining {i}")