Hello, I’m just learning how to use ML-Agents after watching a bunch of YouTube videos of people making walking legs and whatnot. I found a 20x20 maze model online and set up an agent to receive a small reward for the first time it explores a tile and a large reward for reaching the center. The agent is in control of a ball that is able to roll around the environment. I have a script set up to spawn the ball on the outer edge of the maze to prevent any overfitting and currently the training works well however it does reach its max total step count fairly quickly (20,000,000 total steps, 10,000-20,000 episode step count, 8 duplicated maze environments) At 20 mil steps of training the ball sometimes reaches the center if spawning near it, and if not it just explores the spawn area around it
My main question: How long should i expect the agent to train for in terms of total max steps?
I could just increase the max step count to 100 mil or something like that and see if it works but I also want to make sure my reward structure is efficient and I’m not over training my agent. Any feedback would be amazing, also if more info is needed just let me know and I can share more about my project.
Hello,
Training an ML-Agents agent effectively depends on several factors, including your reward structure, observation space, action space, and the complexity of your environment. Here are some thoughts on your current setup and how you can optimize the process:
Training Duration
20M Steps: For complex environments like a maze, 20M steps may be insufficient depending on the difficulty of the task. Maze navigation often requires more exploration and learning, especially if the agent has to generalize to different starting positions.
Recommended Step Range: In general, agents for moderately complex tasks can require anywhere from 50M to 500M steps. For a 20x20 maze with random spawning, aiming for 100M steps initially seems reasonable.
Reward Structure
The reward structure plays a crucial role in guiding the agent’s learning. Here’s a quick evaluation of your setup:
Small Reward for Exploring Tiles:
Pro: Encourages exploration of the environment.
Con: If the reward for exploration outweighs the reward for reaching the center, the agent might focus too much on exploring rather than solving the maze.
Suggestion: Consider adding a slight penalty for repeated exploration of the same tiles to encourage the agent to explore the entire maze rather than lingering in one area.
Large Reward for Reaching the Center:
Pro: Directly incentivizes completing the maze.
Con: Ensure this reward is significantly larger than the exploration rewards. A common ratio is to make the terminal reward at least 10x larger than cumulative exploration rewards.
Suggestion: Add a time-based penalty (negative reward per step) to encourage the agent to solve the maze faster.
Environment Design
Multiple Mazes: To prevent overfitting, consider using a set of randomly generated mazes instead of one fixed maze. This ensures the agent learns generalizable navigation strategies.
Dynamic Start Locations: You’ve already done this, which is great. Ensure the spawn positions are truly random and cover the entire outer edge uniformly.
Observation Space:
Ensure the agent has a sufficient representation of the environment. For example, raycasts or local spatial grids can help it “see” the walls and paths around it.
If using a grid-like representation, ensure the resolution is adequate for a 20x20 maze.
Hyperparameter Tuning
Max Steps per Episode: Ensure your step count allows the agent enough time to reach the center but terminates if it’s clear the agent is stuck or looping.
Learning Rate: Check if your learning rate is allowing the agent to improve steadily. Too high might lead to unstable learning, while too low might slow down progress.
Exploration (Epsilon): Ensure the agent maintains a balance between exploration (trying new paths) and exploitation (using known paths).
Signs of Inefficient Training
Reward Plateau: If the average reward doesn’t increase over several million steps, it could mean your agent is either stuck in local optima or the reward structure isn’t effectively guiding it.
Repeating Behaviors: If the agent repeats behaviors without solving the maze, it might not be receiving adequate feedback to improve.
Testing Efficiency
Reward Clarity: Test your reward structure by running a simplified version of the environment (e.g., a smaller maze). Ensure the agent behaves logically and achieves the goal.
Behavior Analysis: Use the ML-Agents’ built-in TensorBoard or visualization tools to inspect the agent’s decision-making process during training.
Next Steps
Increase Total Steps: Incrementally increase the total training steps to 50M, 100M, and beyond while monitoring the training progress.
Experiment with Rewards: Adjust the exploration and center-reaching rewards to ensure the agent prioritizes solving the maze.
Add Penalties: Introduce penalties for inefficiency (e.g., step count penalties or repeated exploration).
Diversify Environments: Test the agent on different maze configurations to enhance generalization. Official Website
By iteratively refining your setup, you should see noticeable improvements in the agent’s performance over time.
I hope this info will helps you .
Best Regards
merry867