seller_agent.critic.load_state_dict(mdict['critic'])
seller_agent.critic_target.load_state_dict(mdict['critic_target'])
# In[10]:
for e in range(epochs):
seller_agent.reservation_price = generate_seller_prices_paper(1)[0]
sellers = []
for ii, p in enumerate(generate_seller_prices_paper(19)):
sellers.append(LinearBlackBoxSeller('s' + str(ii), p, noisy=True))
sellers.append(seller_agent)
buyers = []
for ii, p in enumerate(generate_buyer_prices_paper(20)):
buyers.append(LinearBlackBoxBuyer('b' + str(ii), p, noisy=True))
agents = sellers + buyers
for ag in agents:
ag.new_game()
ROUNDS_PER_GAME = 10
market_env = MarketEnvironment(
sellers=sellers,
buyers=buyers,
max_time=10, ## not the same as rounds per game!!
matcher=RandomMatcher(reward_on_reference=True))
init_observation = market_env.reset()
# Define the initial number of agents, the number of rounds and games
n_sellers = 100
n_buyers = 100
n_game = 1
n_round = 10
# Create initial agents with names and reservation prices
# All agents are the same for now
res_prices = generate_seller_prices_paper(discrete=False, count=n_sellers)
names = ['Seller ' + str(i) for i in range(1, n_sellers + 1)]
sellers = np.array([
LinearBlackBoxSeller(agent_id=names[i],
reservation_price=res_prices[i],
noisy=True) for i in range(n_sellers)
])
res_prices = generate_buyer_prices_paper(discrete=False, count=n_buyers)
names = ['Buyer ' + str(i) for i in range(1, n_buyers + 1)]
buyers = np.array([
LinearBlackBoxBuyer(agent_id=names[i],
reservation_price=res_prices[i],
noisy=True) for i in range(n_buyers)
])
# For plotting
fig, ax = plt.subplots(figsize=(8, 8), tight_layout=True)
ax.set_xlim(75, 150)
# Loop over games
for g in range(1):
print(
"GAME", g,