def run_model(game_count=1):
"""
run model for game_count games
"""
# Make environment
env = WhaleEnv(
config={
'active_player': 0,
'seed': datetime.utcnow().microsecond,
'env_num': 1,
'num_players': 5
})
# Set up agents
action_num = 3
agent = SimpleAgent(action_num=action_num, player_num=5)
agent_0 = RandomAgent(action_num=action_num)
agent_1 = RandomAgent(action_num=action_num)
agent_2 = RandomAgent(action_num=action_num)
agent_3 = RandomAgent(action_num=action_num)
agents = [agent, agent_0, agent_1, agent_2, agent_3]
env.set_agents(agents)
agent.load_pretrained()
for game in range(game_count):
# Generate data from the environment
trajectories = env.run(is_training=False)
# Print out the trajectories
print('\nEpisode {}'.format(game))
i = 0
for trajectory in trajectories:
print('\tPlayer {}'.format(i))
[print(t) for t in trajectory]
i += 1
def train_model(max_episodes=100):
"""
Trains a DQN agent to play the CartPole game by trial and error
:return: None
"""
# buffer = ReplayBuffer()
# Make environment
env = WhaleEnv(
config={
'active_player': 0,
'seed': datetime.utcnow().microsecond,
'env_num': 1,
'num_players': 5
})
# Set up agents
action_num = 3
agent = SimpleAgent(action_num=action_num, player_num=5)
agent_0 = NoDrawAgent(action_num=action_num)
agent_1 = NoDrawAgent(action_num=action_num)
agent_2 = NoDrawAgent(action_num=action_num)
agent_3 = NoDrawAgent(action_num=action_num)
# agent_train = RandomAgent(action_num=action_num)
agents = [agent, agent_0, agent_1, agent_2, agent_3]
# train_agents = [agent_train, agent_0, agent_1, agent_2, agent_3]
env.set_agents(agents)
agent.load_pretrained()
min_perf, max_perf = 1.0, 0.0
for episode_cnt in range(1, max_episodes + 1):
# print(f'{datetime.utcnow()} train ...')
loss = agent.train(
collect_gameplay_experiences(env, agents, GAME_COUNT_PER_EPISODE))
# print(f'{datetime.utcnow()} eval ...')
avg_rewards = evaluate_training_result(env, agents,
EVAL_EPISODES_COUNT)
# print(f'{datetime.utcnow()} calc ...')
if avg_rewards[0] > max_perf:
max_perf = avg_rewards[0]
agent.save_weight()
if avg_rewards[0] < min_perf:
min_perf = avg_rewards[0]
print('{0:03d}/{1} perf:{2:.2f}(min:{3:.2f} max:{4:.2f})'
'loss:{5:.4f} rewards:{6:.2f} {7:.2f} {8:.2f} {9:.2f}'.format(
episode_cnt, max_episodes, avg_rewards[0], min_perf,
max_perf, loss[0], avg_rewards[1], avg_rewards[2],
avg_rewards[3], avg_rewards[4]))
# env.close()
print('training end')