🧪 What Would I Do (in your system specifically)

No generic RL fluff. Just targeted moves.

1. 🔥 Micro-exploration injection (your own idea, but sharper)

Right now:

epsilon = 0
epsilonNice = 0.006

Try:

Option A (cleanest)

• Keep epsilon = 0
• Increase epsilonNice → 0.01

👉 doubles intervention rate 👉 still minimal, but enough to shake habits

Option B (more surgical)

Make epsilonNice adaptive:

if stagnation_detected:
    p_value = 0.01
else:
    p_value = 0.006

Trigger on:

• no new high score for X episodes
• OR mean declining

if episodes_since_last_high_score > N: trigger_exploration_boost() —

2. 🧠 Reduce sequence length ceiling

Your gearbox climbs too high.

Try capping at:

max_gear = 8  # instead of 10+

Why:

• Forces more reactive decision making
• Reduces overfitting to long trajectories

3. 🪣 Replay memory bias tweak

Right now: passive distribution

Try:

• Slight priority to higher-score buckets

Not full PER. Just bias.

👉 Tell the agent:

“These rare good runs matter more.”

4. ⚡ Learning rate pulse

You’re at:

LR = 0.002

Late stage trick:

• Temporarily bump:

0.002 → 0.003 (short burst)

👉 helps escape local minima 👉 then drop back