Generalized Advantage Estimation (GAE)

Definition

Generalized Advantage Estimation (GAE) is a bias-variance balancing technique for estimating the advantage function in reinforcement learning, proposed by Schulman et al. in 2015. GAE provides low-variance but nearly unbiased advantage estimates for policy gradient algorithms (such as PPO and A2PPO) by computing exponentially weighted averages of multiple temporal difference (TD) residuals.

Background: Advantage Function and TD Residuals

In Actor-Critic reinforcement learning, the advantage function is defined as:

A^\pi(s_t, a_t) = Q^\pi(s_t, a_t) - V^\pi(s_t)

Direct computation requires knowledge of the true value function $V^\pi$ , which in practice must be approximated. The simple one-step TD advantage estimate is:

A_t^{(1)} = \delta_t = r_t + \gamma V(s_{t+1}) - V(s_t)

However, one-step estimates have low variance but high bias (due to reliance on inaccurate value estimates). $n$ -step returns can reduce bias but increase variance.

GAE Definition

GAE balances bias and variance through exponentially weighted averaging of $n$ -step TD residuals:

\hat{A}_t^{\text{GAE}(\lambda, \gamma)} = \sum_{k=0}^{\infty} (\gamma\lambda)^{k} \delta_{t+k}

where $\lambda \in [0,1]$ controls the bias-variance tradeoff:

$\lambda = 0$ : Degenerates to one-step TD (low variance, high bias)
$\lambda = 1$ : Similar to $n$ -step returns (low bias, high variance)

In practice, due to finite horizon, the recursive form is used:

\hat{A}_t = \delta_t + \gamma\lambda(1-d_t)\hat{A}_{t+1}

where $d_t$ is the termination signal ( $d_t=1$ indicates episode termination at step $t$ ).

Application in A2PPO

In the A2PPO algorithm, GAE is used for advantage estimation with the following hyperparameter settings:

Parameter	Value	Meaning
$\gamma$	0.99	Discount factor
$\lambda$ (GAE- $\lambda$ )	0.915	GAE parameter

In A2PPO's ablation experiments, the combination of GAE with the attention mechanism produces more stable policy gradient estimates, significantly outperforming Vanilla PPO (final reward $1071.41 \pm 7.75$ vs $344.87 \pm 563.71$ ).

GAE's Variance Control Mechanism

GAE's variance control stems from its finite memory property: distant future TD residuals decay exponentially as $(\gamma\lambda)^k$ . More importantly, GAE's variance is positively correlated with $\lambda$ — increasing $\lambda$ increases estimation bias but reduces variance, as more reliance is placed on actual cumulative returns.

A2PPO (Attention-Augmented PPO): The application framework for GAE in cislunar trajectory optimization
Low-Thrust Transfer MDP: The RL problem formulation that GAE serves

References

Schulman J, Moritz P, Levine S, et al. High-dimensional continuous control using generalized advantage estimation[J]. arXiv:1512.04455, 2015.
Ul Haq I U, Dai H, Du C. Autonomous low-thrust trajectory optimization in cislunar space via attention-augmented reinforcement learning[J]. Aerospace Science and Technology, 2026.