Fixing VMAS AssertionError: MultiDiscrete Action Space Guide

Let's dive into tackling a tricky error you might encounter when working with the Vectorized Multi-Agent Simulator (VMAS) and its MultiDiscrete action spaces. Specifically, we're going to break down an AssertionError that pops up due to inconsistencies in how VMAS handles action sizes. If you're wrestling with this, you're in the right place! We'll dissect the problem, understand why it happens, and explore potential solutions. Let's get started, guys!

Understanding the Problem: The AssertionError in VMAS

At its core, the AssertionError in VMAS arises from a mismatch between the expected and actual shape of the action space. When you define a MultiDiscrete action space, you're essentially telling VMAS that your agents can perform multiple discrete actions simultaneously. For instance, gymnasium.spaces.MultiDiscrete([3] * 13) means each agent has 13 different action components, and each component can take on 3 possible values (0, 1, or 2). So, what's the issue? The problem surfaces when VMAS's internal methods disagree on how to interpret this action space.

Specifically, the error often manifests in the step method, which is the heart of any reinforcement learning environment. This method takes the agent's actions as input and advances the simulation by one step. The AssertionError we're discussing here usually looks something like this: AssertionError: Action for agent agent_0 has shape 13, but should have shape 2. This message is a cry for help, indicating a serious conflict within VMAS. To put it simply, the error message is telling us that the action provided has a shape of 13, but the shape expected is 2. This usually happens when the action size is interpreted differently in get_random_action and get_agent_action_size.

To understand this better, let's break down the two key methods involved:

1. get_random_action: This method is responsible for generating random actions, often used for exploration or initialization. When multidiscrete_actions is set to True, this method correctly accesses the action space using self.get_agent_action_space(agent) and determines the number of components based on action_space.shape[0]. For our example of MultiDiscrete([3] * 13), this correctly identifies 13 components and generates actions with a shape of [num_envs, 13], where num_envs is the number of parallel environments.

2. get_agent_action_size: This method, on the other hand, is used within the step method to validate the actions provided. The error arises because get_agent_action_size is incorrectly returning a value of 2, despite the action space clearly defining 13 components. This discrepancy is the core of the problem. The shape mismatch occurs in the step method's assertion, which checks if actions[i].shape[1] == self.get_agent_action_size(self.agents[i]). Since actions[i].shape[1] is 13 and self.get_agent_action_size returns 2, the assertion fails, and the error is raised.

This internal inconsistency within VMAS highlights a critical bug. It's like having two parts of a machine speaking different languages – they can't agree on how the action space is structured. The crux of the issue lies in how VMAS internally manages and accesses action space information. It seems that get_random_action correctly interprets the action_space.shape[0] (which should be 13 in our case), while get_agent_action_size is somehow misinterpreting or accessing the action size information, leading to the erroneous value of 2. Understanding this conflict is the first step towards finding a solution.

Dissecting the Code: Where the Error Originates

To really nail down this AssertionError, let's dig deeper into the code snippets involved. We'll focus on how VMAS handles action spaces and where the discrepancy arises. By examining the get_random_action and get_agent_action_size methods, we can pinpoint the exact location of the bug and understand why it's happening.

First, let's revisit the get_random_action method. As we mentioned earlier, this method correctly interprets the MultiDiscrete action space. When multidiscrete_actions is True, it retrieves the action space using self.get_agent_action_space(agent). This is the correct way to access the action space associated with a specific agent. The method then uses action_space.shape[0] to determine the number of components in the action space. This is crucial because action_space.shape[0] directly reflects the number of dimensions in the MultiDiscrete space – in our example, it's 13. So far, so good. The method proceeds to generate random actions with the correct shape: [num_envs, 13], with each component taking a value between 0 and 2, based on action_space.nvec. This part of the code is working as expected.

The trouble begins when we look at get_agent_action_size. This method is called within the step method to validate the actions provided by the agent. The step method is the core of the interaction loop in a reinforcement learning environment, so any error here can halt the entire training process. The critical line of code where the AssertionError occurs is the assertion: actions[i].shape[1] == self.get_agent_action_size(self.agents[i]). This line is checking if the shape of the action provided by the agent matches the expected action size. However, self.get_agent_action_size is returning the wrong value. It's returning 2, while the actual action shape is [num_envs, 13]. This discrepancy triggers the assertion failure and the dreaded AssertionError.

To understand why get_agent_action_size is returning the incorrect value, we need to examine its implementation. Unfortunately, without access to the internal code of VMAS, we can only speculate. However, based on the behavior we're observing, it seems that this method is either:

• Accessing the action space information in a different way than get_random_action.
• Storing the action space size incorrectly.
• Making an incorrect assumption about the structure of the action space.

It's possible that get_agent_action_size is looking at a different attribute or variable that doesn't accurately reflect the number of components in the MultiDiscrete space. Or, perhaps there's a bug in how the action space size is initialized or updated internally. The key takeaway here is that the root cause lies in the inconsistent handling of action space information within VMAS. This inconsistency creates a conflict between what the agent is providing (actions with shape [num_envs, 13]) and what the step method expects (actions with shape [num_envs, 2]).

Why Can't I Fix It Directly? Limitations and External Dependencies

Okay, so we've pinpointed the issue: a discrepancy in how VMAS methods interpret the MultiDiscrete action space size. The natural question is,