def markovDecision(layout, circle):
env = SnakesAndLadder(layout, circle)
agent = RandomAgent(env.action_space)
n_episodes = 50
for episode in range(n_episodes):
state = env.reset()
done = False
while not done:
action = agent.select_action(state)
next_state, reward, done = env.step(action)
agent.update(state, action, reward, next_state)
state = next_state
possible_actions = [0, 1] # Cooperate or Defect
cooperator, defector = RandomAgent(possible_actions, p=0.9), RandomAgent(possible_actions, p=0.1)
# Stateless interactions (agents do not have memory)
s = None
n_iter = 1000
for i in range(n_iter):
# A full episode:
done = False
while not done:
# Agents decide
a0 = cooperator.act()
a1 = defector.act()
# World changes
new_s, (r0, r1), done, _ = env.step(([a0], [a1]))
# Agents learn
cooperator.update(s, (a0, a1), (r0, r1), new_s )
defector.update(s, (a1, a0), (r1, r0), new_s )
s = new_s
print(r0, r1)
env.reset()
