Create reinforcement_learning_with_transfer_learning_and_meta_learnin…

…g.py

ai_engine/reinforcement_learning_with_transfer_learning_and_meta_learning.py

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+import gym
+import ray
+import tensorflow as tf
+
+class ReinforcementLearningAgentWithTransferLearningAndMetaLearning:
+    def __init__(self, env_name, pre_trained_model, meta_learning_model):
+        self.env = gym.make(env_name)
+        self.agent = ray.agent.PPOAgent(self.env.observation_space, self.env.action_space)
+        self.pre_trained_model = pre_trained_model
+        self.meta_learning_model = meta_learning_model
+
+    def train(self, num_episodes=1000):
+        for episode in range(num_episodes):
+            observation = self.env.reset()
+            done = False
+            rewards = 0
+            while not done:
+                action = self.agent.act(observation)
+                observation, reward, done, _ = self.env.step(action)
+                rewards += reward
+            print(f'Episode {episode+1}, Reward: {rewards}')
+
+        # Transfer learning
+        self.agent.set_weights(self.pre_trained_model.get_weights())
+
+        # Meta-learning
+        self.meta_learning_model.fit(self.agent.get_weights())
+
+    def act(self, observation):
+        return self.agent.act(observation)