implementing-random-agent

name	implementing-random-agent
description	Develop a baseline random agent to stress-test the environment and establish minimum performance bounds.
version	0.1.0

Implementing the Random Baseline Agent

Before any deep neural networks consume GPU cycles, a perfectly random agent must be deployed inside the finalized Gymnasium environment. This serves two critical purposes: stability testing and establishing the absolute zero-point of intelligence.

Step 1 — Environment Instantiation

Load the custom env.py.

• Apply all intended wrappers (TimeLimit, Observation wrappers).

Step 2 — Constructing the Random Loop

Write a script that queries env.action_space.sample() without replacing any logic.

• Run for exactly 1,000,000 continuous steps.
• Goal: Guarantee the environment does not throw memory leaks, indexing errors, or segmentation faults during extended arbitrary rollouts.

Step 3 — Record Minimum Baseline Score

Track the episode_reward across 1,000 episodes completed by the random agent.

• Calculate the Mean and Standard Deviation.
• This is the "Zero Intelligence" score. If a complex RL algorithm scores lower than this, learning has entirely collapsed.

Output Format

Produce a python script (baseline_random.py) and log the metrics in the output document:

### Random Agent Baseline
- **Hardware Stability**: [Passed 1M steps / Failed with error X]
- **Mean Episode Return**: [-14.5 ± 2.1]
- **Average Episode Length**: [140 steps]