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
def train_clac(training_tag):
env = gym.make(ENVIRONMENT_NAME)
env = DummyVecEnv([lambda: env])
print("hidden values before: ", env.unwrapped.envs[0].hiddenValues)
data = pd.DataFrame()
if (isinstance(training_tag, float)):
model = CLAC(clac_MlpPolicy,
env,
ent_coef=training_tag,
verbose=1,
policy_kwargs=POLICY_KWARGS)
for step in range(TRAINING_STEPS):
env.reset()
(model, learning_results) = model.learn(
total_timesteps=TRAINING_TIMESTEPS, log_interval=100)
file_tag = str(training_tag).replace(".", "p")
if (SAVE_AGENTS):
model.save("nchain/models/CLAC_" + ENVIRONMENT_NAME + "_s" +
str(step) + "_t" + str(file_tag) + "_i" +
str(CURRENT_ITERATION) + "_ts" +
str(TRAINING_TIMESTEPS))
if (SAVE_FINAL_AGENT):
model.save("nchain/models/CLAC_" + ENVIRONMENT_NAME + "_t" +
str(file_tag) + "_i" + str(CURRENT_ITERATION) + "_ts" +
str(TRAINING_STEPS * TRAINING_TIMESTEPS))
del env
del model
step = 0
return data
def train(training_tag):
env = gym.make(ENVIRONMENT_NAME)
env = DummyVecEnv([lambda: env])
data = pd.DataFrame()
#env._max_episode_steps = 200
if(isinstance(training_tag, float)):
model = CLAC(clac_MlpPolicy, env, mut_inf_coef=training_tag, verbose=VERBOSITY, policy_kwargs = POLICY_KWARGS)
for step in range(TRAINING_STEPS):
#print("length normal: ", env.unwrapped.envs[0].length)
(model, learning_results) = model.learn(total_timesteps=TRAINING_TIMESTEPS, log_interval=100)
#data = data.append(learning_results, ignore_index=True)
data = data.append(test(model, "CLAC" + str(training_tag), training_tag, False, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "CLAC" + str(training_tag), training_tag, 1, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "CLAC" + str(training_tag), training_tag, 2, (step + 1) * TRAINING_TIMESTEPS))
file_tag = str(training_tag).replace(".", "p")
if(SAVE_AGENTS):
model.save(SAVE_FOLDER + "/models/CLAC_" + ENVIRONMENT_NAME + "_s" + str(step) + "_t" + str(file_tag) + "_i" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_TIMESTEPS))
if(SAVE_FINAL_AGENT):
model.save(SAVE_FOLDER + "/models/CLAC_" + ENVIRONMENT_NAME + "_t" + str(file_tag) + "_i" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_STEPS * TRAINING_TIMESTEPS))
env.reset()
del model
step = 0
model = SAC(sac_MlpPolicy, env, ent_coef=training_tag, verbose=VERBOSITY, policy_kwargs = POLICY_KWARGS)
for step in range(TRAINING_STEPS):
(model, learning_results) = model.learn(total_timesteps=TRAINING_TIMESTEPS, log_interval=100)
#data = data.append(learning_results, ignore_index=True)
data = data.append(test(model, "SAC" + str(training_tag), training_tag, False, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "SAC" + str(training_tag), training_tag, 1, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "SAC" + str(training_tag), training_tag, 2, (step + 1) * TRAINING_TIMESTEPS))
file_tag = str(training_tag).replace(".", "p")
if(SAVE_AGENTS):
model.save(SAVE_FOLDER + "/models/SAC_" + ENVIRONMENT_NAME + "_s" + str(step) + "_t" + str(file_tag) + "_i" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_TIMESTEPS))
if(SAVE_FINAL_AGENT):
model.save(SAVE_FOLDER + "/models/SAC_" + ENVIRONMENT_NAME + "_t" + str(file_tag) + "_i" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_STEPS * TRAINING_TIMESTEPS))
env.reset()
del model
if(training_tag == "CLAC"):
model = CLAC(clac_MlpPolicy, env, verbose=VERBOSITY, policy_kwargs = POLICY_KWARGS)
for step in range(TRAINING_STEPS):
(model, learning_results) = model.learn(total_timesteps=TRAINING_TIMESTEPS, log_interval=100)
#data = data.append(learning_results, ignore_index=True)
data = data.append(test(model, "CLAC", "auto", False, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "CLAC", "auto", 1, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "CLAC", "auto", 2, (step + 1) * TRAINING_TIMESTEPS))
if(SAVE_AGENTS):
model.save(SAVE_FOLDER + "/models/CLAC_" + ENVIRONMENT_NAME + "_s" + str(step) + "_auto" + "_i" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_TIMESTEPS))
if(SAVE_FINAL_AGENT):
model.save(SAVE_FOLDER + "/models/CLAC_" + ENVIRONMENT_NAME + "_t" + "_auto" + "_i" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_STEPS * TRAINING_TIMESTEPS))
env.reset()
del model
if(training_tag == "SAC"):
model = SAC(sac_MlpPolicy, env, verbose=VERBOSITY, policy_kwargs = POLICY_KWARGS)
for step in range(TRAINING_STEPS):
(model, learning_results) = model.learn(total_timesteps=TRAINING_TIMESTEPS, log_interval=100)
#data = data.append(learning_results, ignore_index=True)
data = data.append(test(model, "SAC", "auto", False, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "SAC", "auto", 1, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "SAC", "auto", 2, (step + 1) * TRAINING_TIMESTEPS))
if(SAVE_AGENTS):
model.save(SAVE_FOLDER + "/models/SAC_" + ENVIRONMENT_NAME + "_s" + str(step) + "_auto" + "_i" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_TIMESTEPS))
if(SAVE_FINAL_AGENT):
model.save(SAVE_FOLDER + "/models/SAC_" + ENVIRONMENT_NAME + "_t" + "_auto" + "_i" + str(CURRENT_ITERATION) + "_ts" + str( TRAINING_STEPS * TRAINING_TIMESTEPS))
env.reset()
del model
if(training_tag == "DDPG"):
# the noise objects for DDPG
n_actions = env.action_space.shape[-1]
param_noise = None
action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions))
model = DDPG(DDPG_MlpPolicy, env, verbose=VERBOSITY, param_noise=param_noise, action_noise=action_noise, policy_kwargs = POLICY_KWARGS)
for step in range(TRAINING_STEPS):
(model, learning_results) = model.learn(total_timesteps=TRAINING_TIMESTEPS, log_interval=100)
#data = data.append(learning_results, ignore_index=True)
data = data.append(test(model, "DDPG", None, False, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "DDPG", None, 1, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "DDPG", None, 2, (step + 1) * TRAINING_TIMESTEPS))
if(SAVE_AGENTS):
model.save(SAVE_FOLDER + "/models/DDPG_" + ENVIRONMENT_NAME + "_s" + str(step) + "_i" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_TIMESTEPS))
if(SAVE_FINAL_AGENT):
model.save(SAVE_FOLDER + "/models/DDPG_" + ENVIRONMENT_NAME + "_t" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_STEPS * TRAINING_TIMESTEPS))
env.reset()
del model
if(training_tag == "PPO1"):
model = PPO1(MlpPolicy, env, verbose=VERBOSITY, policy_kwargs = POLICY_KWARGS)
for step in range(TRAINING_STEPS):
model.learn(total_timesteps=TRAINING_TIMESTEPS, log_interval=100)
data = data.append(test(model, "PPO1", training_tag, False, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "PPO1", training_tag, 1, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "PPO1", training_tag, 2, (step + 1) * TRAINING_TIMESTEPS))
if(SAVE_AGENTS):
model.save(SAVE_FOLDER + "/models/PPO1_" + ENVIRONMENT_NAME + "_s" + str(step) + "_i" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_TIMESTEPS))
if(SAVE_FINAL_AGENT):
model.save(SAVE_FOLDER + "/models/PPO1_" + ENVIRONMENT_NAME + "_t" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_STEPS * TRAINING_TIMESTEPS))
env.reset()
del model
if(training_tag == "A2C"):
model = A2C(MlpPolicy, env, verbose=VERBOSITY, policy_kwargs = POLICY_KWARGS)
for step in range(TRAINING_STEPS):
model.learn(total_timesteps=TRAINING_TIMESTEPS, log_interval=100)
data = data.append(test(model, "A2C", training_tag, False, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "A2C", training_tag, 1, (step + 1) * TRAINING_TIMESTEPS))
data = data.append(test(model, "A2C", training_tag, 2, (step + 1) * TRAINING_TIMESTEPS))
if(SAVE_AGENTS):
model.save(SAVE_FOLDER + "/models/A2C_" + ENVIRONMENT_NAME + "_s" + str(step) + "_i" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_TIMESTEPS))
if(SAVE_FINAL_AGENT):
model.save(SAVE_FOLDER + "/models/A2C_" + ENVIRONMENT_NAME + "_t" + str(CURRENT_ITERATION) + "_ts" + str(TRAINING_STEPS * TRAINING_TIMESTEPS))
env.reset()
del model
return data
def test_agent(agent_step):
now = time.time()
for coef_index in range(len(CLAC_COEFS)):
mut_coef = CLAC_COEFS[coef_index]
ent_coef = SAC_COEFS[coef_index]
training_timestep = 0
clac_env = gym.make(ENVIRONMENT_NAME)
clac_env = DummyVecEnv([lambda: clac_env])
clac_model = CLAC(CLAC_MlpPolicy, clac_env, mut_inf_coef=mut_coef, verbose=1)
sac_env = gym.make(ENVIRONMENT_NAME)
sac_env = DummyVecEnv([lambda: sac_env])
sac_model = SAC(MlpPolicy, sac_env, ent_coef=ent_coef, verbose=1)
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-3, verbose=1)
for resample_step in range(0, NUM_RESAMPLES):
features = pd.DataFrame()
if(agent_step == 1):
print(mut_coef, " ", ent_coef, " ", NUM_TRAINING_STEPS, " ", ENVIRONMENT_NAME, " ", FOLDER, " ", resample_step)
(clac_model, learning_results) = clac_model.learn(total_timesteps=NUM_TRAINING_STEPS, log_interval=1000)
(sac_model, learning_results) = sac_model.learn(total_timesteps=NUM_TRAINING_STEPS, log_interval=1000)
(mirl_model, learning_results) = mirl_model.learn(total_timesteps=NUM_TRAINING_STEPS, log_interval=1000)
# Save models
clac_model.save(FOLDER + "/Training/models/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step))
sac_model.save(FOLDER + "/Training/models/CLAC_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step))
mirl_model.save(FOLDER + "/Training/models/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step))
training_timestep += NUM_TRAINING_STEPS
# Test Normal
eval_results = eval_model(clac_model, clac_env, "CLAC", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 0)
eval_results.to_pickle(FOLDER + "/Training/results/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
eval_results = eval_model(sac_model, sac_env, "SAC", ent_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 0)
eval_results['AgentID'] = agent_step
eval_results.to_pickle(FOLDER + "/Training/results/SAC_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
eval_results = eval_model(mirl_model, mirl_env, "MIRL", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 0)
eval_results['AgentID'] = agent_step
eval_results.to_pickle(FOLDER + "/Training/results/MIRL_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
# Test generalization
eval_results = eval_model(clac_model, clac_env, "CLAC", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 1)
eval_results['AgentID'] = agent_step
eval_results.to_pickle(FOLDER + "/Generalization/results/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
eval_results = eval_model(sac_model, sac_env, "SAC", ent_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 1)
eval_results['AgentID'] = agent_step
eval_results.to_pickle(FOLDER + "/Generalization/results/SAC_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
eval_results = eval_model(mirl_model, mirl_env, "MIRL", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 1)
eval_results['AgentID'] = agent_step
eval_results.to_pickle(FOLDER + "/Generalization/results/MIRL_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
# Test generalization Extreme
eval_results = eval_model(clac_model, clac_env, "CLAC", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 2)
eval_results['AgentID'] = agent_step
eval_results.to_pickle(FOLDER + "/Extreme/results/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
eval_results = eval_model(sac_model, sac_env, "SAC", ent_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 2)
eval_results['AgentID'] = agent_step
eval_results.to_pickle(FOLDER + "/Extreme/results/SAC_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
eval_results = eval_model(mirl_model, mirl_env, "MIRL", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 2)
eval_results['AgentID'] = agent_step
eval_results.to_pickle(FOLDER + "/Extreme/results/MIRL_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
clac_env.env_method("reset_features")
sac_env.env_method("reset_features")
mirl_env.env_method("reset_features")
del sac_model
del sac_env
del clac_model
del clac_env
del mirl_model
del mirl_env
later = time.time()
difference = int(later - now)
print("Tested Agent Time: ", difference)
features = pd.DataFrame()
mirl_env = gym.make(ENVIRONMENT_NAME)
mirl_env = DummyVecEnv([lambda: mirl_env])
print("here 3")
mirl_model = CLAC(CLAC_MlpPolicy,
mirl_env,
mut_inf_coef=mut_coef,
coef_schedule=0.99,
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_TEST_" +
str(mut_coef).replace(".", "p") + "_" +
str(agent_step) + "_0.pkl")
print("here 3")
print("here 5")
mirl_model.save(FOLDER + "/models/MIRL_TEST_" +
str(mut_coef).replace(".", "p") + "_" + str(agent_step) +
"_0")
del mirl_model
del mirl_env
env.unwrapped.envs[0].setHiddenValues([0.60, 0.40, 0.48823553, 0.50263806, 0.4893053, 0.49855249, 0.51247362, 0.51375429, 0.49918321, 0.48824743])
TOTAL_TIMESTEPS = 100000
SAMPLE_SIZE = 100000
fig, axes = plt.subplots(nrows=2, ncols=5, sharey=True, sharex=True)
clac_axes = axes[0,:]
sac_axes = axes[1,:]
for _ in range(10):
for model_index, ent_coef in enumerate([0.0, 1.0, 2.0, 3.0]):
model = CLAC(clac_MlpPolicy, env, mut_inf_coef=ent_coef, verbose=0, policy_kwargs = POLICY_KWARGS)
(model, learning_results) = model.learn(total_timesteps=TOTAL_TIMESTEPS, log_interval=100)
for state in [0,1]:
clac_sample = []
for _ in range(SAMPLE_SIZE):
clac_action = model.predict([state])[0][0][0]
clac_action = (clac_action + 1)/(2) # Normalize the [-1,1] action to [0,1], gym required actions spaces to be symmetric.
clac_sample.append(clac_action)
clac_sample_array = np.asarray(clac_sample)
mu_clac = clac_sample_array.mean()
std_clac = clac_sample_array.std()
print("CLAC: ent_coef: ", ent_coef, " state: ", state, " mean: ", mu_clac, "std: ", std_clac)
def test_agent(agent_step):
now = time.time()
for coef_index in range(len(CLAC_COEFS)):
mut_coef = CLAC_COEFS[coef_index]
ent_coef = SAC_COEFS[coef_index]
training_timestep = 0
if(agent_step == 1):
print(mut_coef, " ", ent_coef, " ", NUM_TRAINING_STEPS, " ", ENVIRONMENT_NAME, " ", FOLDER)
features = pd.DataFrame()
clac_env = gym.make(ENVIRONMENT_NAME)
clac_env = DummyVecEnv([lambda: clac_env])
clac_model = CLAC(CLAC_MlpPolicy, clac_env, mut_inf_coef=mut_coef, verbose=1)
sac_env = gym.make(ENVIRONMENT_NAME)
sac_env = DummyVecEnv([lambda: sac_env])
sac_model = SAC(MlpPolicy, sac_env, ent_coef=ent_coef, verbose=1)
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-3, verbose=1)
(clac_model, learning_results) = clac_model.learn(total_timesteps=NUM_TRAINING_STEPS, log_interval=1000)
learning_results['AgentID'] = agent_step
learning_results.to_pickle(FOLDER + "/Training/results/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_0.pkl")
(sac_model, learning_results) = sac_model.learn(total_timesteps=NUM_TRAINING_STEPS, log_interval=1000)
learning_results['AgentID'] = agent_step
learning_results.to_pickle(FOLDER + "/Training/results/SAC_"+ str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_0.pkl")
(mirl_model, learning_results) = mirl_model.learn(total_timesteps=NUM_TRAINING_STEPS, log_interval=1000)
learning_results['AgentID'] = agent_step
learning_results.to_pickle(FOLDER + "/Training/results/MIRL_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_0.pkl")
training_timestep += NUM_TRAINING_STEPS
sac_env.env_method("set_features", env_features)
mirl_env.env_method("set_features", env_features)
#if(agent_step == 0):
# print(env_features)
Power = env_features[0]
Density = env_features[1]
Friction = env_features[2]
Gravity = env_features[3]
d = {"Mut Coefficient": mut_coef, "Ent Coefficient": ent_coef, "Resample Step":resample_step, "Power": Power, "Density": Density, "Friction": Friction, "Gravity": Gravity}
#d = {"Mut Coefficient": mut_coef, "Resample Step":resample_step, "Power": Power, "Density": Density, "Friction": Friction, "Gravity": Gravity}
features = features.append(d, ignore_index = True)
# Train generalization
eval_results = eval_model(clac_model, env, "CLAC", mut_coef, NUM_TESTING_STEPS, training_timestep, 0)
eval_results['AgentID'] = agent_step
eval_results.to_pickle(FOLDER + "/Generalization/results/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_0.pkl")
eval_results = eval_model(sac_model, env, "SAC", ent_coef, NUM_TESTING_STEPS, training_timestep, 0)
eval_results['AgentID'] = agent_step
eval_results.to_pickle(FOLDER + "/Generalization/results/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_0.pkl")
eval_results = eval_model(mirl_model, env, "MIRL", mut_coef, NUM_TESTING_STEPS, training_timestep, 0)
eval_results['AgentID'] = agent_step
eval_results.to_pickle(FOLDER + "/Generalization/results/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_0.pkl")
clac_model.save(FOLDER + "/Training/models/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_0")
sac_model.save(FOLDER + "/Training/models/SAC_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_0")
mirl_model.save(FOLDER + "/Training/models/MIRL_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_0")
#features.to_pickle(FOLDER + "/features/features_" + str(agent_step) + "_" + str(mut_coef) + "_" + str(ent_coef) + ".pkl")
for resample_step in range(1, NUM_RESAMPLES):
if(agent_step == 1):
print(mut_coef, " ", ent_coef, " ", NUM_TRAINING_STEPS, " ", ENVIRONMENT_NAME, " ", FOLDER, " resample step ", resample_step)
(clac_model, learning_results) = clac_model.learn(total_timesteps=NUM_TRAINING_STEPS, reset_num_timesteps=False, log_interval=1000)
learning_results.to_pickle(FOLDER + "/Training/results/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
(sac_model, learning_results) = sac_model.learn(total_timesteps=NUM_TRAINING_STEPS, reset_num_timesteps=False, log_interval=1000)
learning_results.to_pickle(FOLDER + "/Training/results/SAC_"+ str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
(mirl_model, learning_results) = mirl_model.learn(total_timesteps=NUM_TRAINING_STEPS, reset_num_timesteps=False, log_interval=1000)
learning_results.to_pickle(FOLDER + "/Training/results/MIRL_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl")
training_timestep += NUM_TRAINING_STEPS
clac_model.save(FOLDER + "/Training/models/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step))
sac_model.save(FOLDER + "/Training/models/SAC_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step))
mirl_model.save(FOLDER + "/Training/models/MIRL_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step))
#print(features)
del sac_model
del sac_env
del clac_model
del clac_env
del mirl_model
del mirl_env
later = time.time()
difference = int(later - now)
print("Tested Agent Time: ", difference)
features = pd.DataFrame()
clac_env = gym.make(ENVIRONMENT_NAME)
clac_env = DummyVecEnv([lambda: clac_env])
clac_model = CLAC(CLAC_MlpPolicy,
clac_env,
mut_inf_coef=mut_coef,
verbose=1)
sac_env = gym.make(ENVIRONMENT_NAME)
sac_env = DummyVecEnv([lambda: sac_env])
sac_model = SAC(MlpPolicy, sac_env, ent_coef=ent_coef, verbose=1)
(clac_model,
learning_results) = clac_model.learn(total_timesteps=NUM_TRAINING_STEPS,
log_interval=1000)
learning_results['AgentID'] = agent_step
learning_results.to_pickle(FOLDER + "/results/CLAC_" +
str(mut_coef).replace(".", "p") + "_" +
str(agent_step) + "_0.pkl")
(sac_model,
learning_results) = sac_model.learn(total_timesteps=NUM_TRAINING_STEPS,
log_interval=1000)
learning_results['AgentID'] = agent_step
learning_results.to_pickle(FOLDER + "/results/SAC_" +
str(ent_coef).replace(".", "p") + "_" +
str(agent_step) + "_0.pkl")
for resample_step in range(1, NUM_RESAMPLES):
# Set both environments to the same resampled values
def test_agent(agent_step):
for coef_index in range(len(CLAC_COEFS)):
mut_coef = CLAC_COEFS[coef_index]
ent_coef = SAC_COEFS[coef_index]
if (agent_step == 1):
print(mut_coef, " ", ent_coef, " ", NUM_TRAINING_STEPS, " ",
ENVIRONMENT_NAME, " ", FOLDER)
features = pd.DataFrame()
clac_env = gym.make(ENVIRONMENT_NAME)
clac_env = DummyVecEnv([lambda: clac_env])
clac_model = CLAC(CLAC_MlpPolicy,
clac_env,
mut_inf_coef=mut_coef,
verbose=1)
sac_env = gym.make(ENVIRONMENT_NAME)
sac_env = DummyVecEnv([lambda: sac_env])
sac_model = SAC(MlpPolicy, sac_env, ent_coef=ent_coef, verbose=1)
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)
(clac_model, learning_results) = clac_model.learn(
total_timesteps=NUM_TRAINING_STEPS, log_interval=1000)
learning_results['AgentID'] = agent_step
learning_results.to_pickle(FOLDER + "/results/CLAC_" +
str(mut_coef).replace(".", "p") + "_" +
str(agent_step) + "_0.pkl")
(sac_model, learning_results) = sac_model.learn(
total_timesteps=NUM_TRAINING_STEPS, log_interval=1000)
learning_results['AgentID'] = agent_step
learning_results.to_pickle(FOLDER + "/results/SAC_" +
str(ent_coef).replace(".", "p") + "_" +
str(agent_step) + "_0.pkl")
(mirl_model, learning_results) = mirl_model.learn(
total_timesteps=NUM_TRAINING_STEPS, log_interval=1000)
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"):
clac_env.env_method("randomize", 0)
elif (RANDOMIZATION_LEVEL == "Random"):
clac_env.env_method("randomize", 1)
elif (RANDOMIZATION_LEVEL == "Extreme"):
clac_env.env_method("randomize", 2)
elif (RANDOMIZATION_LEVEL == "Test"):
clac_env.env_method("randomize", -1)
else:
print("Error resampling unknown value: ", RANDOMIZATION_LEVEL)
continue
env_features = clac_env.env_method("get_features")[0]
sac_env.env_method("set_features", env_features)
mirl_env.env_method("set_features", env_features)
if (agent_step == 1):
print(env_features)
Power = env_features[0]
Density = env_features[1]
Friction = env_features[2]
Gravity = env_features[3]
d = {
"Mut Coefficient": mut_coef,
"Ent Coefficient": ent_coef,
"Resample Step": resample_step,
"Power": Power,
"Density": Density,
"Friction": Friction,
"Gravity": Gravity
}
features = features.append(d, ignore_index=True)
(clac_model, learning_results) = clac_model.learn(
total_timesteps=NUM_TRAINING_STEPS,
reset_num_timesteps=False,
log_interval=1000)
learning_results.to_pickle(FOLDER + "/results/CLAC_" +
str(mut_coef).replace(".", "p") + "_" +
str(agent_step) + "_" +
str(resample_step) + ".pkl")
(sac_model, learning_results) = sac_model.learn(
total_timesteps=NUM_TRAINING_STEPS,
reset_num_timesteps=False,
log_interval=1000)
learning_results.to_pickle(FOLDER + "/results/SAC_" +
str(ent_coef).replace(".", "p") + "_" +
str(agent_step) + "_" +
str(resample_step) + ".pkl")
(mirl_model, learning_results) = mirl_model.learn(
total_timesteps=NUM_TRAINING_STEPS,
reset_num_timesteps=False,
log_interval=1000)
learning_results.to_pickle(FOLDER + "/results/MIRL_" +
str(mut_coef).replace(".", "p") + "_" +
str(agent_step) + "_" +
str(resample_step) + ".pkl")
clac_model.save(FOLDER + "/models/CLAC_" +
str(mut_coef).replace(".", "p") + "_" +
str(agent_step) + "_0")
sac_model.save(FOLDER + "/models/SAC_" +
str(ent_coef).replace(".", "p") + "_" +
str(agent_step) + "_0")
mirl_model.save(FOLDER + "/models/MIRL_" +
str(mut_coef).replace(".", "p") + "_" +
str(agent_step) + "_0")
features.to_pickle(FOLDER + "/features/features_" + str(agent_step) +
"_" + str(mut_coef) + "_" + str(ent_coef) + ".pkl")
#print(features)
del sac_model
del sac_env
del clac_model
del clac_env
del mirl_model
del mirl_env