Merging the experience buffers of two separately trained agents to continue training a single agent is a practical approach to leverage distributed learning experiences. However, directly merging experience buffers is not a built-in feature in MATLAB's Reinforcement Learning Toolbox as of my last update. You will need to implement a custom solution to achieve this. Here's a general outline of steps you can follow:
1. Extract Experience Buffers
First, you need to access the experience buffers from both agents. Depending on the type of agent you are using (e.g., DQN, DDPG, SAC, etc.), the method to access the experience buffer might differ. For many agents, the experience buffer is stored in the ExperienceBuffer property of the agent's AgentOptions.
% Assuming agent1 and agent2 are your trained agents
buffer1 = agent1.AgentOptions.ExperienceBuffer;
buffer2 = agent2.AgentOptions.ExperienceBuffer;
2. Merge the Experience Buffers
Once you have access to both buffers, you need to merge them. This might involve concatenating the experiences stored in these buffers. An experience typically includes states, actions, rewards, next states, and done flags. The exact structure depends on the agent type.
% This is a conceptual step; actual implementation will depend on the buffer's structure
mergedBuffer = mergeBuffers(buffer1, buffer2);
The mergeBuffers function is something you would need to implement. It should handle the concatenation of experiences from both buffers while respecting the maximum buffer size, if applicable.
3. Create a New Agent with the Merged Buffer
After merging the buffers, you will need to create a new agent (or choose one of the existing agents) and replace its experience buffer with the merged buffer. This step also depends on the type of agent and how it allows for manipulation of its experience buffer.
% Example for setting the merged buffer back to an agent
% This is conceptual; actual implementation might differ
agent1.AgentOptions.ExperienceBuffer = mergedBuffer;
4. Continue Training
Now that your chosen agent has an experience buffer that includes experiences from both original agents, you can continue training this agent.
trainingOptions = rlTrainingOptions(...);
% Use the environment setup you have
env = yourEnvironmentSetupFunction();
% Continue training
trainingStats = train(agent1, env, trainingOptions);
