Reinforcement-Learning Chemotherapy Dosing Agent

Training Controls

Learning rate α 0.30

Exploration decay 0.985

Toxicity aversion 1.0×

Training speed 6 steps/s

Live Stats

Episode

Epsilon ε

1.00

Avg reward (20 ep)

0.0

Cure rate (20 ep)

Best action: no dose

Best action: low dose

Best action: medium dose

Best action: high dose

How it works

A simulated patient has a discretized tumor-burden level (0 = cured … 5 = severe) and a toxicity level (0 = none … 4 = dose-limiting). Each step the agent picks a dose a ∈ {none, low, medium, high}. The environment updates stochastically:

tumor' = clamp(tumor − killRate(a) + noise, 0, 5)
tox'   = clamp(tox + toxRate(a) − recovery, 0, 4)
reward = −(tumor' + toxAversion · tox')
       + 50 if tumor' = 0 (cured, episode ends)
       − 50 if tox' ≥ 4  (dose-limiting toxicity, episode ends)

The agent has no model of these dynamics — it learns a state–action value table Q(s,a) purely from trial and error via the tabular Q-learning (Bellman) update:

Q(s,a) ← Q(s,a) + α·[ r + γ·max_a′ Q(s′,a′) − Q(s,a) ]

with learning rate α, discount γ = 0.9, and ε-greedy action selection: with probability ε it explores a random dose, otherwise it exploits the currently best-known dose. ε starts at 1.0 and multiplies by the decay factor after every episode, so exploration fades as the policy improves.

Grid landscape — one column per (tumor, toxicity) state; height = the best known Q-value there, colour = the currently best action.
Marble — the agent's current state; it hops to the next column as each simulated day passes.
Toxicity aversion — scales how harshly the reward punishes toxicity, letting you push the learned policy from aggressive to conservative dosing.

This mirrors real reinforcement-learning approaches to adaptive treatment regimes and closed-loop dosing research, where a policy is learned from simulated or real patient trajectories rather than hand-coded rules — always as a decision-support aid, never as an unsupervised prescriber.

Tumor: 5 · Toxicity: 0 Action: —

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