Memory Agent: Implementation

A Memory Agent needs a structured way to store, retrieve, and use past experiences to influence future decisions. This involves:

1. Encoding and Storing New Experiences (Adding to memory)
2. Retrieving Relevant Memories (Using memory for decision-making)
3. Applying Retrieved Memories to Decision-Making (Choosing actions based on memory)

1. Adding New Experiences to Memory

When an agent experiences an event (e.g., interacting with an environment or another agent), it needs to store the relevant information in its memory.

Steps for Memory Addition

1. Perceive the environment → Collect relevant features from the current situation.
2. Encode the experience into a high-dimensional vector.
3. Store it in Short-Term Memory (STM).
4. Periodically compress and move old STM entries into Intermediate Memory (IM) and Long-Term Memory (LTM).

Implementation Approach

def add_experience(self, state, action, reward, next_state):
    """Encodes and adds an experience to STM."""
    experience = np.concatenate([state, [action, reward], next_state])  # Encode experience as a vector
    self.stm.append(experience)  # Add to STM

    if len(self.stm) > self.stm_capacity:
        self.transition_memory()  # Move older experiences to IM and LTM

2. Retrieving Relevant Memories

When the agent needs to make a decision, it should retrieve past experiences that are most relevant to the current situation.

Steps for Memory Retrieval

1. Encode the current state as a vector.
2. Search for the most similar memories (using cosine similarity or a learned retrieval function).
3. Prioritize STM (most recent and highest-resolution memories).
4. If STM lacks useful data, query IM and LTM for general patterns.

Implementation Approach

def retrieve_memory(self, query_state):
    """Finds the most relevant past experience based on similarity."""
    query_vector = np.array(query_state)
    best_match = None
    best_score = float('-inf')

    def cosine_similarity(a, b):
        return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-8)

    # Search STM first
    for memory in self.stm:
        similarity = cosine_similarity(query_vector, memory[:len(query_state)])
        if similarity > best_score:
            best_score = similarity
            best_match = memory

    # Search IM if no good match in STM
    if best_match is None:
        for memory in self.im:
            similarity = cosine_similarity(query_vector, memory[:len(query_state)])
            if similarity > best_score:
                best_score = similarity
                best_match = memory

    # Search LTM if no match in IM
    if best_match is None:
        for memory in self.ltm:
            similarity = cosine_similarity(query_vector, memory[:len(query_state)])
            if similarity > best_score:
                best_score = similarity
                best_match = memory

    return best_match  # Return the most similar past experience

3. Using Memory for Decision-Making

Once a past experience is retrieved, the agent can use it to inform its next action.

Decision-Making Process

1. Retrieve similar past experience based on the current state.
2. Extract the best action taken in that past experience.
3. Decide whether to reuse the past action or modify it based on updated conditions.

Implementation Approach

def make_decision(self, state):
    """Uses retrieved memory to decide on an action."""
    retrieved_memory = self.retrieve_memory(state)

    if retrieved_memory is None:
        # No relevant memory found → take a random action
        return np.random.choice(self.action_space)

    # Extract the action that was taken in the past similar situation
    past_action = retrieved_memory[len(state)]
    return past_action  # Reuse past successful action

4. Enhancements for Smarter Decision-Making

A. Action Adjustment

Instead of directly copying past actions, the agent can:

• Weigh the past action based on memory similarity (use softmax over top-k memories).
• Modify the past action slightly (to account for changes in environment dynamics).

def make_decision_with_adjustment(self, state):
    """Retrieves similar memories and adjusts actions using a weighted approach."""
    retrieved_memories = [self.retrieve_memory(state) for _ in range(3)]  # Get top 3 memories
    actions = [mem[len(state)] for mem in retrieved_memories if mem is not None]
    
    if not actions:
        return np.random.choice(self.action_space)

    # Choose most frequent or best-rated action from similar memories
    adjusted_action = max(set(actions), key=actions.count)
    return adjusted_action

B. Reinforcement Learning Hybrid

• Use the memory retrieval function as a lookup table for Q-learning.
• Store (state, action, reward, next state) tuples and refine decisions using reinforcement learning.

5. Summary of Memory Integration in Agent Decision-Making

| Step | Description | Implementation | |——|————|—————-| | 1. Add Memory | Store new experiences in STM and gradually compress older ones. | add_experience() | | 2. Retrieve Memory | Find the most relevant past experience. | retrieve_memory() | | 3. Decision-Making | Use memory to determine the best action. | make_decision() | | 4. Adjust Actions (Optional) | Modify actions based on memory weighting. | make_decision_with_adjustment() | | 5. Reinforcement Learning (Optional) | Use memory as a Q-learning lookup table. | Extend to RL framework |

6. Potential Applications

• Agent-based simulations: Helps agents remember past strategies in long-term simulations.
• Game AI: Allows AI players to recall past moves and adjust strategies.
• Reinforcement Learning: Enhances learning efficiency by providing a structured memory system.
• Autonomous decision-making: Enables AI assistants and robots to adapt based on experience.

Next Steps

• Test the memory-based decision-making in a simple game simulation.
• Evaluate the retrieval accuracy and decision efficiency.
• Extend to reinforcement learning environments to compare with traditional learning techniques.