In reinforcement-learning schedulers for LEO satellite task allocation, the exploration-exploitation setting is a driver of battery degradation in its own right, not merely a convergence parameter: at FIXED delivered mission value, a more exploratory scheduler needs more orbits AND discharges more deeply per orbit, so integrated aging cost is a monotone increasing function of the exploration coefficient. Measured in simulation (corrected 3 Oct 2026 after fixing an argmax tie-breaking bug; an earlier single-seed figure of 2.33x is withdrawn): median 1.47x and 1.58x the damage at eps=1 versus eps=0 for the same delivered value, across 30 independent task streams in two battery models (0/30 reversals). The earlier 55%/45% cycle-count versus depth split is not claimed. Note what is NOT claimed: cycles per DAY are pinned by orbital mechanics (15.58 at 400 km) and no scheduler changes them; it is cycles per MISSION that rise. FALSIFIED IF replaying one fixed task stream at several exploration coefficients, each run to the same cumulative reward, shows no monotone relationship between the coefficient and integrated aging.

In decentralized LEO satellite constellations performing distributed task scheduling via a coalition-formation game, an agentic reinforcement learning (RL) controller whose exploration policy is guided by Upper-Confidence-Bound (UCB) acquisition over a surrogate Bayesian-optimization (BO) model of task-assignment value will (a) reduce the mean deviation from coalition-structure-stable equilibria (measured as normalized Shapley-value regret) by ≥20% relative to a baseline decentralized RL controller (e.g., independent PPO or greedy auction) under identical constellation, traffic, and battery models, AND (b) exhibit a statistically significant (p<0.05, paired test across ≥30 simulated episodes/seeds) positive correlation between the controller's exploration rate (UCB confidence width / entropy of action distribution) and an unmodeled/hidden battery-aging cost metric (cumulative depth-of-discharge-weighted cycle stress from onboard compute-induced power draw), such that higher exploration produces measurably higher aging cost per unit of scheduling-quality gain.

Minimum viable test (MVT): a discrete-event LEO constellation simulator (walker-delta, 66–100 sats) with a coalition-formation task-scheduling layer, comparing 3 controllers (baseline greedy/auction, independent PPO, UCB-BO-guided agentic RL) across 30 randomized seeds, 3 traffic regimes (steady, bursty, emergency-surge), and a calibrated battery-aging cost model, run for 500 simulated orbits (~750–1000 hrs simulated time) per condition. Primary endpoints: (1) normalized equilibrium-deviation regret, (2) exploration-rate vs. aging-cost correlation, (3) Pareto frontier of scheduling-quality vs. battery-aging cost across exploration hyperparameter sweep (UCB β ∈ {0.1,...,5}).

# Pseudocode: Agentic RL + UCB-guided surrogate BO for coalition scheduling

for episode in range(N_EPISODES):
    env.reset(constellation_config, traffic_regime, battery_state)
    surrogate_gp = GaussianProcess(kernel=Matern52)  # BoTorch/GPyTorch
    for orbit_step in range(N_ORBITS):
        for sat in env.satellites:
            state = sat.get_state()  # SoC, queue, position, ISL links
            # Surrogate BO proposes candidate task-assignment values
            candidate_actions = sample_candidates(state)
            mu, sigma = surrogate_gp.predict(candidate_actions)
            ucb_score = mu + beta * sigma
            action = argmax(ucb_score)  # exploration-guided choice

            reward, next_state, aging_cost = env.step(sat, action)
            surrogate_gp.update(candidate_actions[action], reward)

            # Outer RL policy (PPO) updates using reward - lambda*aging_cost
            rl_agent.store_transition(state, action, reward, aging_cost, next_state)

        rl_agent.update()  # PPO gradient step across coalition
        coalition_regret = compute_shapley_regret(env.coalition_state)
        log(coalition_regret, aging_cost, ucb_beta, exploration_entropy)

# Post-hoc analysis
correlation = spearman(exploration_entropy_series, aging_cost_series)
pareto_frontier = compute_pareto(regret_series, aging_cost_series, over=ucb_beta_sweep)