Algorithm 2.

Adaptive Behavioral Multi-Agent Algorithm (ABMA)

Require: ${\left\{{\tilde{x}}_{t_i}^a\right\}}_{i=0}^{n_x^a}$, ${\left\{{\tilde{u}}_{{\tau }_i}^a\right\}}_{i=0}^{n_u^a}$, ${\left\{{\pi }_i^a\right\}}_{i=0}^{T-1}$ for a ∈ 𝒜
1:	for all a ∈ 𝒜 do
2:	compute $\left({\widehat{x}}_T^a,{\widehat{\theta }}_T^a\right)={\text{argmax}}_{\left(x_T,{\theta }_T\right)}{\psi }_T\left(x_T,{\theta }_T\right)$
3:	for all v ∈ V do
4:	set ${\pi }_v^a\in \mathrm{arg\; min}\left\{{\phi }^a\left({\widehat{x}}_T,{\widehat{\theta }}_T,{\left\{{\pi }_i\right\}}_{i=T}^{T+n-1}\right)|{\left\{{\pi }_i\right\}}_{i=T}^{T+n-1}\in S_v\right\}$
5:	set ${\varphi }_v^a={\phi }^a\left({\widehat{x}}_T^a,{\widehat{\theta }}_T^a,{\pi }_v^a\right)$
6:	end for
7:	end for
8:	compute $y\in \text{argmin}\{{\sum }_{a\in 𝒜}{\sum }_{v\in V}{\varphi }_v^a·y_v^a:{\sum }_{a\in 𝒜}{\sum }_{v\in V}v·y_v^a\le \beta ,{\sum }_{v\in V}{y}_v^a=1$ for a ∈ 𝒜, $y_v^a\in \left\{0,1\right\}$ for a, v ∈ 𝒜 × V}
9:	for all a ∈ 𝒜 and v ∈ V do
10:	set ${\pi }_{ABMA}^a(T)={\pi }_v^a$ if $y_v^a=1$
11:	end for
12:	return ${\pi }_{ABMA}^a(T)$ for a ∈ 𝒜