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solutionhackathon.py
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import numpy as np
from reinforcement_learning_framework import ActorCriticAgent
from noc_simulator import NOCSimulator # Assuming NOCSimulator is the simulator for network-on-chip environment
def train_rl_agent(env, agent, total_episodes):
for episode in range(total_episodes):
state = env.reset()
agent.reset()
while True:
action = agent.act(state)
next_state, reward, done = env.step(action)
agent.learn(state, action, reward, next_state, done)
state = next_state
if done:
break
def main():
# Initialize the simulation environment
env = NOCSimulator()
# Initialize the RL agent with optimized memory usage
agent = ActorCriticAgent(state_size=env.state_size, action_size=env.action_size, memory_efficient=True)
# Train the RL agent
total_episodes = 1000
train_rl_agent(env, agent, total_episodes)
# Save the trained model
agent.save('optinoc_actor_critic_model.h5')
if __name__ == "__main__":
main()