# Info info = Info(env) # create the info object info.print_info() # print out information # TO NOTE: # Each observation is a stack of 3 states. # Each game state has 8 variables # making each observation have the size 24 # set action and state action_size, state_size, num_agents = info.getInfo() # baseline = Baseline(env, action_size, state_size) # baseline.run() seed = 6 random.seed(seed) torch.manual_seed(seed) # Create the maddpg object maddpg = MADDPG(env, state_size, action_size, num_agents, seed) # train agent # scores, average_scores_list = maddpg.train(n_episodes=5000) # info.plotResults(scores, average_scores_list) # plot the scores # test best agent maddpg.test(env, state_size)
if __name__ == "__main__":
# Configuration
n_episodes = 1
checkpoint = "./checkpoints/checkpoint{}.pth"
# Unitiy environment
env = UnityEnvironment("./Tennis_Linux/Tennis.x86_64")
# Agent
agent = TennisMultiAgent(state_size=24, action_size=2, n_agents=2)
agent.load(checkpoint)
# DDPG
maddpg = MADDPG(env=env, agent=agent)
scores = maddpg.test(n_episodes=n_episodes)
# Close the environment
env.close()
if n_episodes > 1:
# Show results
print(scores)
print("Average score of {} episodes: {:.2f}".format(
n_episodes, np.mean(scores)))
# Plot scores
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(np.linspace(1, n_episodes + 1, n_episodes), scores)
ax.set_xlabel("Episodes")
ax.set_ylabel("Score per episode")
]
# handle invalid dir char
for i in range(len(model_names)):
model_names[i] = model_names[i].replace('[', '').replace(']', '').replace(
' ', '').replace(',', '_')
# handle standard arg, i.e., {}
model_names = ['standard' if name == '' else name for name in model_names]
# model loop
for i in trange(len(args), desc='model', leave=True):
model_dir = '{}/{}'.format(root, model_names[i])
os.mkdir(model_dir)
# log cmd
with open('{}/cmd_config.txt'.format(model_dir), 'w') as f:
for k, v in control_args.items():
f.write(str(k) + ': ' + str(v) + '\n')
arg = args[i]
# repeat loop
for n in trange(control_args['repeat'], desc='repeat', leave=True):
dir = '{}/{}'.format(model_dir, n)
os.mkdir(dir)
maddpg = MADDPG(env, **arg)
if control_args.has_key('load'):
model_path = control_args['load']
maddpg.load_actor(model_path)
maddpg.load_critic(model_path)
if control_args['train']:
maddpg.train(dir, control_args['save_interval'])
maddpg.save(dir)
maddpg.test(dir, n=control_args['n_test'])
