def test_agent(agent_step):
for coef_index in range(len(CLAC_COEFS)):
mut_coef = CLAC_COEFS[coef_index]
if (agent_step == 1):
print(mut_coef, " ", NUM_TRAINING_STEPS, " ", ENVIRONMENT_NAME,
" ", FOLDER)
features = pd.DataFrame()
mirl_env = gym.make(ENVIRONMENT_NAME)
mirl_env = DummyVecEnv([lambda: mirl_env])
mirl_model = CLAC(CLAC_MlpPolicy,
mirl_env,
mut_inf_coef=mut_coef,
coef_schedule=3.3e-4,
verbose=1)
(mirl_model, learning_results) = mirl_model.learn(
total_timesteps=NUM_TRAINING_STEPS, log_interval=10)
learning_results['AgentID'] = agent_step
learning_results.to_pickle(FOLDER + "/results/MIRL_" +
str(mut_coef).replace(".", "p") + "_" +
str(agent_step) + "_0.pkl")
for resample_step in range(1, NUM_RESAMPLES):
# Set both environments to the same resampled values
if (RANDOMIZATION_LEVEL == "Normal"):
mirl_env.env_method("randomize", 0)
elif (RANDOMIZATION_LEVEL == "Extreme"):
mirl_env.env_method("randomize", 1)
elif (RANDOMIZATION_LEVEL == "Test"):
mirl_env.env_method("randomize", -1)
else:
print("Error resampling unknown value: ", RANDOMIZATION_LEVEL)
continue
if (agent_step == 1):
print(mut_coef, " ", NUM_TRAINING_STEPS, " ",
ENVIRONMENT_NAME, " ", FOLDER, " resample step ",
resample_step)
(mirl_model, learning_results) = mirl_model.learn(
total_timesteps=NUM_TRAINING_STEPS,
reset_num_timesteps=False,
log_interval=10)
learning_results.to_pickle(FOLDER + "/results/MIRL_" +
str(mut_coef).replace(".", "p") + "_" +
str(agent_step) + "_" +
str(resample_step) + ".pkl")
mirl_model.save(FOLDER + "/models/MIRL_" +
str(mut_coef).replace(".", "p") + "_" +
str(agent_step) + "_0")
del mirl_model
del mirl_env