num_of_actions=2,
actor_only=ACTOR_ONLY,
alfa=0.4,
lambd=0.1013,
epsilon=0.519)
pellet_action = model.states[0].actions[0]
chow_action = model.states[0].actions[1]
pellet_action._g = 15.1
pellet_action._n = 13.0
chow_action._g = 0.9
chow_action._n = 0.0
control = model.act(state=0, kn=1, da=0.5, raw=True)
# κN = 0.7507, corresponding to blocking of D2 receptors with an efficiency ofroughly 25%
depleted = model.act(state=0, kn=0.7507, da=0.5, raw=True)
txt = f"""
| | Pellet | Chow |
|-----------------------|---------|------|
| Control | | |
| Replication | {round(control[0], 4)} | {round(control[1], 4)} |
| Fig 6 | 1.0 | 0.4 |
| --------------------- | ------- | ---- |
| D2R Depleted | | |
| Replication | {round(depleted[0], 4)} | {round(depleted[1], 4)} |
| Fig 6 | -0.6 | 0.4 |
"""
print(txt)
