def analyze_agent_kl(exp_key):
"""
Evaluates the agent KL post-hoc for a given experiment.
Args:
exp_key (str): the experiment ID
"""
# load the experiment
comet_api = comet_ml.API(api_key=LOADING_API_KEY)
experiment = comet_api.get_experiment(project_name=PROJECT_NAME,
workspace=WORKSPACE,
experiment=exp_key)
# create the environment
param_summary = experiment.get_parameters_summary()
env_name = [a for a in param_summary if a['name'] == 'env'][0]['valueCurrent']
env = create_env(env_name)
# create the agent
asset_list = experiment.get_asset_list()
agent_config_asset_list = [a for a in asset_list if 'agent_args' in a['fileName']]
agent_args = None
if len(agent_config_asset_list) > 0:
# if we've saved the agent config dict, load it
agent_args = experiment.get_asset(agent_config_asset_list[0]['assetId'])
agent_args = json.loads(agent_args)
agent_args = agent_args if 'opt_type' in agent_args['inference_optimizer_args'] else None
agent = create_agent(env, agent_args=agent_args)[0]
# get the list of checkpoint timesteps
ckpt_asset_list = [a for a in asset_list if 'ckpt' in a['fileName']]
ckpt_asset_names = [a['fileName'] for a in ckpt_asset_list]
ckpt_timesteps = [int(s.split('ckpt_step_')[1].split('.ckpt')[0]) for s in ckpt_asset_names]
ckpt_timesteps = list(np.sort(ckpt_timesteps)[::CKPT_SUBSAMPLE])
agent_kls = []
# initial episode using random init
prev_episode, _, _ = collect_episode(env, agent)
for ckpt_ind, ckpt_timestep in enumerate(ckpt_timesteps):
# load the checkpoint
print('Evaluating checkpoint ' + str(ckpt_ind + 1) + ' of ' + str(len(ckpt_timesteps)))
load_checkpoint(agent, exp_key, ckpt_timestep)
# evaluate agent KL
print(' Evaluating agent KL...')
agent_kls.append(estimate_agent_kl(env, agent, prev_episode))
print(' Done.')
# collect an episode
print(' Collecting episode...')
prev_episode, _, _ = collect_episode(env, agent)
print(' Done.')
return {'steps': ckpt_timesteps, 'agent_kl': np.array(agent_kls)}
def compare_policies(exp_key1, exp_key2, write_result=True):
"""
Compares the policies of two agents at the end of training.
Args:
exp_key1 (str):
exp_key2 (str):
write_result (bool)
"""
# load the experiments
comet_api = comet_ml.API(api_key=LOADING_API_KEY)
exp1 = comet_api.get_experiment(project_name=PROJECT_NAME,
workspace=WORKSPACE,
experiment=exp_key1)
exp2 = comet_api.get_experiment(project_name=PROJECT_NAME,
workspace=WORKSPACE,
experiment=exp_key2)
# create the environment
param_summary = exp1.get_parameters_summary()
env_name = [a for a in param_summary if a['name'] == 'env'][0]['valueCurrent']
env1 = create_env(env_name)
env2 = create_env(env_name)
# create the agents
asset_list = exp1.get_asset_list()
agent_config_asset_list = [a for a in asset_list if 'agent_args' in a['fileName']]
agent_args = None
if len(agent_config_asset_list) > 0:
# if we've saved the agent config dict, load it
agent_args = exp1.get_asset(agent_config_asset_list[0]['assetId'])
agent_args = json.loads(agent_args)
agent_args = agent_args if 'opt_type' in agent_args['inference_optimizer_args'] else None
agent1 = create_agent(env1, agent_args=agent_args)[0]
load_checkpoint(agent1, exp_key1)
asset_list = exp2.get_asset_list()
agent_config_asset_list = [a for a in asset_list if 'agent_args' in a['fileName']]
agent_args = None
if len(agent_config_asset_list) > 0:
# if we've saved the agent config dict, load it
agent_args = exp2.get_asset(agent_config_asset_list[0]['assetId'])
agent_args = json.loads(agent_args)
agent_args = agent_args if 'opt_type' in agent_args['inference_optimizer_args'] else None
agent2 = create_agent(env1, agent_args=agent_args)[0]
load_checkpoint(agent2, exp_key2)
# evaluate the KL between policies
kl12 = []
kl21 = []
agent1.reset(); agent1.eval()
agent2.reset(); agent2.eval()
state1 = env1.reset()
state2 = env2.reset()
for state_ind in range(N_STATES):
# perform policy optimization on state1
action1 = agent1.act(state1)
agent2.act(state1)
kl = kl_divergence(agent1.approx_post, agent2.approx_post).sum().detach().item()
kl12.append(kl)
agent1.reset(); agent1.eval()
agent2.reset(); agent2.eval()
# perform policy optimization on state2
agent1.act(state2)
action2 = agent2.act(state2)
kl = kl_divergence(agent2.approx_post, agent1.approx_post).sum().detach().item()
kl21.append(kl)
# step the environments
state1, _, done1, _ = env1.step(action1)
state2, _, done2, _ = env2.step(action2)
if done1:
agent1.reset(); agent1.eval()
state1 = env1.reset()
done1 = False
if done2:
agent2.reset(); agent2.eval()
state2 = env2.reset()
done2 = False
kls = {'kl12': kl12,
'kl21': kl21}
if write_result:
pickle.dump(kls, open('policy_kl_' + exp_key1 + '_' + exp_key2 + '.p', 'wb'))
return kls
def compare_goal_optimizers(model_exp_key,
opt_exp_key=None,
write_results=True,
stochastic_model=False):
"""
Optimize random goal states using a model-based estimator.
Train the policy optimizer online. Compare with other optimizers.
Note: tailored to HalfCheetah-v2 environment currently.
Args:
model_exp_key (str): model-based experiment key
opt_exp_key (str): optimizer experiment key. If None, trains from scratch
write_results (bool): whether to pickle results directly
stochastic_model (bool): whether to sample states or use mean estimate
train_model (bool) whether to train the model online
"""
## MODEL
# load the model experiment
comet_api = comet_ml.API(api_key=LOADING_API_KEY)
experiment = comet_api.get_experiment(project_name=PROJECT_NAME,
workspace=WORKSPACE,
experiment=model_exp_key)
# create the environment
param_summary = experiment.get_parameters_summary()
env_name = [a for a in param_summary
if a['name'] == 'env'][0]['valueCurrent']
env = create_env(env_name)
# create a synchronous env to parallelize training
sync_env = SynchronousEnv(env, BATCH_SIZE)
# create the agent
asset_list = experiment.get_asset_list()
agent_config_asset_list = [
a for a in asset_list if 'agent_args' in a['fileName']
]
agent_args = None
if len(agent_config_asset_list) > 0:
# if we've saved the agent config dict, load it
agent_args = experiment.get_asset(
agent_config_asset_list[0]['assetId'])
agent_args = json.loads(agent_args)
agent_args = agent_args if 'opt_type' in agent_args[
'inference_optimizer_args'] else None
agent = create_agent(env, agent_args=agent_args)[0]
# also, load the most recent episode to sample goal states
asset_times = [
asset['createdAt'] for asset in asset_list
if 'state' in asset['fileName']
]
state_asset = [
a for a in asset_list if a['createdAt'] == max(asset_times)
][0]
episode_states = json.loads(experiment.get_asset(state_asset['assetId']))
# load the checkpoint
load_checkpoint(agent, model_exp_key)
if stochastic_model:
agent.q_value_estimator.state_variable.cond_likelihood.stochastic = True
# swap out the value estimator for goal-based estimator
gb_estimator = GoalBasedQEstimator()
# copy over the dynamics model
gb_estimator.state_likelihood_model = agent.q_value_estimator.state_likelihood_model
gb_estimator.state_variable = agent.q_value_estimator.state_variable
# set the estimator
agent.q_value_estimator = gb_estimator
agent.q_value_estimator.set_goal_std(GOAL_STD)
# agent.alphas['pi'] = 0.
total_results = {
'grad_based': None,
'cem': None,
'it_am': None,
'goal_cond': None
}
goals = []
print('Sampling goals...')
for step_ind in range(N_TOTAL_STEPS):
new_goal_states = np.stack([
episode_states[np.random.randint(0, 25)] for _ in range(BATCH_SIZE)
])
# goal_state = episode_states[goal_ind]
new_goal_states = torch.from_numpy(new_goal_states).float().view(
BATCH_SIZE, -1)
new_goal_states[:, 8:] *= 0.
if step_ind == 0:
goal_state = new_goal_states
else:
# randomly change the goal state with some small probability
flips = (torch.rand(BATCH_SIZE, 1) <
GOAL_FLIP_PROB).float().repeat(1,
new_goal_states.shape[-1])
goal_state = (1 - flips) * goal_state + flips * new_goal_states
goals.append(goal_state)
print('Evaluating gradient-based agent...')
agent.inference_optimizer = GradientBasedInference(lr=1e-3, n_inf_iters=50)
grad_based_results = collect_goal_optimization(agent, sync_env, goals)
total_results['grad_based'] = grad_based_results
print('Done.')
# print('Evaluating CEM agent...')
# agent.inference_optimizer = CEMInference(lr=1e-3, n_top_samples=10, n_inf_iters=50)
# agent.n_action_samples = 100
# cem_results = collect_goal_optimization(agent, sync_env, goals)
# total_results['cem'] = cem_results
# print('Done.')
print('Evaluating iterative amortized agent...')
# create an iterative amortized optimizer
inputs = ['params', 'grads', 'state']
n_input = 24
if 'state' in inputs:
n_input += 17
network_args = {
'type': 'recurrent',
'n_layers': 2,
'inputs': inputs,
'n_units': 512,
'connectivity': 'highway',
'n_input': n_input
}
agent.inference_optimizer = IterativeInferenceModel(
network_args=network_args, n_inf_iters=10)
for m in agent.approx_post.models:
agent.approx_post.models[m] = FullyConnectedLayer(512, 6)
agent.approx_post.gates[m] = FullyConnectedLayer(
512, 6, non_linearity='sigmoid')
agent.approx_post.update = 'iterative'
# create a parameter optimizer for the inference model
inference_parameters = [_ for _ in agent.inference_optimizer.parameters()
] + [_ for _ in agent.approx_post.parameters()]
inf_optim = optim.Adam(inference_parameters, lr=3e-4)
it_am_results = collect_goal_optimization(agent,
sync_env,
goals,
inf_optim=inf_optim)
total_results['it_am'] = it_am_results
print('Done.')
print('Evaluating goal-conditioned agent...')
# create a direct, goal-conditioned network
network_args = {
'type': 'fully_connected',
'n_layers': 2,
'inputs': ['state', 'goal'],
'n_units': 512,
'connectivity': 'highway',
'n_input': 17 + 17
}
agent.inference_optimizer = DirectGoalInferenceModel(
network_args=network_args)
for m in agent.approx_post.models:
agent.approx_post.models[m] = FullyConnectedLayer(512, 6)
agent.approx_post.update = 'direct'
# create a parameter optimizer for the inference model
inference_parameters = [_ for _ in agent.inference_optimizer.parameters()
] + [_ for _ in agent.approx_post.parameters()]
inf_optim = optim.Adam(inference_parameters, lr=3e-4)
goal_cond_results = collect_goal_optimization(agent,
sync_env,
goals,
inf_optim=inf_optim)
total_results['goal_cond'] = goal_cond_results
print('Done.')
if write_results:
pickle.dump(total_results,
open('comp_goal_opt_' + model_exp_key + '.p', 'wb'))
return total_results
def goal_optimization(model_exp_key, opt_exp_key=None, write_results=True):
"""
Optimize random goal states using a model-based estimator.
Note: tailored to HalfCheetah-v2 environment currently.
Args:
model_exp_key (str): model-based experiment key
opt_exp_key (str): optimizer experiment key
write_results (bool): whether to pickle results directly
"""
# load the experiment
comet_api = comet_ml.API(api_key=LOADING_API_KEY)
experiment = comet_api.get_experiment(project_name=PROJECT_NAME,
workspace=WORKSPACE,
experiment=model_exp_key)
# create the environment
param_summary = experiment.get_parameters_summary()
env_name = [a for a in param_summary
if a['name'] == 'env'][0]['valueCurrent']
env = create_env(env_name)
# create the agent
asset_list = experiment.get_asset_list()
agent_config_asset_list = [
a for a in asset_list if 'agent_args' in a['fileName']
]
agent_args = None
if len(agent_config_asset_list) > 0:
# if we've saved the agent config dict, load it
agent_args = experiment.get_asset(
agent_config_asset_list[0]['assetId'])
agent_args = json.loads(agent_args)
agent_args = agent_args if 'opt_type' in agent_args[
'inference_optimizer_args'] else None
agent = create_agent(env, agent_args=agent_args)[0]
# also, load the most recent episode to sample goal states
asset_times = [
asset['createdAt'] for asset in asset_list
if 'state' in asset['fileName']
]
state_asset = [
a for a in asset_list if a['createdAt'] == max(asset_times)
][0]
episode_states = json.loads(experiment.get_asset(state_asset['assetId']))
# load the checkpoint
load_checkpoint(agent, model_exp_key)
# load the optimizer
if opt_exp_key is not None:
# load the experiment
comet_api = comet_ml.API(api_key=LOADING_API_KEY)
opt_experiment = comet_api.get_experiment(project_name=PROJECT_NAME,
workspace=WORKSPACE,
experiment=opt_exp_key)
# create the agent
asset_list = opt_experiment.get_asset_list()
agent_config_asset_list = [
a for a in asset_list if 'agent_args' in a['fileName']
]
agent_args = None
if len(agent_config_asset_list) > 0:
# if we've saved the agent config dict, load it
agent_args = opt_experiment.get_asset(
agent_config_asset_list[0]['assetId'])
agent_args = json.loads(agent_args)
agent_args = agent_args if 'opt_type' in agent_args[
'inference_optimizer_args'] else None
opt_agent = create_agent(env, agent_args=agent_args)[0]
# load the checkpoint
load_checkpoint(opt_agent, opt_exp_key)
agent.inference_optimizer = opt_agent.inference_optimizer
agent.inference_optimizer.n_inf_iters = 20
else:
# create a gradient-based optimizer
agent.inference_optimizer = GradientBasedInference(lr=1e-3,
n_inf_iters=50)
# swap out the value estimator for goal-based estimator
gb_estimator = GoalBasedQEstimator()
# copy over the dynamics model
gb_estimator.state_likelihood_model = agent.q_value_estimator.state_likelihood_model
gb_estimator.state_variable = agent.q_value_estimator.state_variable
# set the estimator
agent.q_value_estimator = gb_estimator
agent.q_value_estimator.set_goal_std(GOAL_STD)
# agent.alphas['pi'] = 0.
# optimize goal states
goal_states = []
traj_states = []
env_states = {'qpos': [], 'qvel': []}
actions = []
inf_objectives = []
agent.reset()
agent.eval()
state = env.reset()
if RENDER:
env.render()
goal_state = None
# goal_ind = 0
print('Collecting goal-optimization episode...')
for step_ind in range(N_TOTAL_STEPS):
print('STEP: ' + str(step_ind))
if step_ind % GOAL_INTERVAL == 0:
goal_state = episode_states[np.random.randint(0, 25)]
# goal_state = episode_states[goal_ind]
goal_state = torch.from_numpy(np.array(goal_state)).float().view(
1, -1)
goal_state[:, 8:] *= 0.
if not TRAJECTORY_FOLLOW:
agent.q_value_estimator.set_goal_state(goal_state)
# goal_ind += 1
if TRAJECTORY_FOLLOW:
# define a sub-goal between current state and goal state
delta_state = goal_state - state
traj_state = state + 0.1 * delta_state
agent.q_value_estimator.set_goal_state(traj_state)
traj_states.append(traj_state)
else:
traj_states.append(goal_states)
goal_states.append(goal_state)
env_states['qpos'].append(copy.deepcopy(env.sim.data.qpos))
env_states['qvel'].append(copy.deepcopy(env.sim.data.qvel))
action = agent.act(state, eval=True)
state, _, _, _ = env.step(action)
inf_objectives.append(agent.inference_optimizer.estimated_objectives)
# import ipdb; ipdb.set_trace()
agent.inference_optimizer.reset(1)
if RENDER:
env.render()
actions.append(action)
print('Done.')
# save the results
results = {
'goal_states': goal_states,
'traj_states': traj_states,
'env_states': env_states,
'actions': actions
}
if write_results:
pickle.dump(results, open('goal_opt_' + model_exp_key + '.p', 'wb'))
return results
def goal_optimization_training(model_exp_key,
opt_exp_key=None,
write_results=True,
stochastic_model=False,
train_model=False):
"""
Optimize random goal states using a model-based estimator.
Train the policy optimizer online.
Note: tailored to HalfCheetah-v2 environment currently.
Args:
model_exp_key (str): model-based experiment key
opt_exp_key (str): optimizer experiment key. If None, trains from scratch
write_results (bool): whether to pickle results directly
stochastic_model (bool): whether to sample states or use mean estimate
train_model (bool) whether to train the model online
"""
# load the experiment
comet_api = comet_ml.API(api_key=LOADING_API_KEY)
experiment = comet_api.get_experiment(project_name=PROJECT_NAME,
workspace=WORKSPACE,
experiment=model_exp_key)
# create the environment
param_summary = experiment.get_parameters_summary()
env_name = [a for a in param_summary
if a['name'] == 'env'][0]['valueCurrent']
env = create_env(env_name)
# create a synchronous env to parallelize training
sync_env = SynchronousEnv(env, BATCH_SIZE)
# create the agent
asset_list = experiment.get_asset_list()
agent_config_asset_list = [
a for a in asset_list if 'agent_args' in a['fileName']
]
agent_args = None
if len(agent_config_asset_list) > 0:
# if we've saved the agent config dict, load it
agent_args = experiment.get_asset(
agent_config_asset_list[0]['assetId'])
agent_args = json.loads(agent_args)
agent_args = agent_args if 'opt_type' in agent_args[
'inference_optimizer_args'] else None
agent = create_agent(env, agent_args=agent_args)[0]
# also, load the most recent episode to sample goal states
asset_times = [
asset['createdAt'] for asset in asset_list
if 'state' in asset['fileName']
]
state_asset = [
a for a in asset_list if a['createdAt'] == max(asset_times)
][0]
episode_states = json.loads(experiment.get_asset(state_asset['assetId']))
# load the checkpoint
load_checkpoint(agent, model_exp_key)
if stochastic_model:
agent.q_value_estimator.state_variable.cond_likelihood.stochastic = True
# load the optimizer
if opt_exp_key is not None:
# load the experiment
comet_api = comet_ml.API(api_key=LOADING_API_KEY)
opt_experiment = comet_api.get_experiment(project_name=PROJECT_NAME,
workspace=WORKSPACE,
experiment=opt_exp_key)
# create the agent
asset_list = opt_experiment.get_asset_list()
agent_config_asset_list = [
a for a in asset_list if 'agent_args' in a['fileName']
]
agent_args = None
if len(agent_config_asset_list) > 0:
# if we've saved the agent config dict, load it
agent_args = opt_experiment.get_asset(
agent_config_asset_list[0]['assetId'])
agent_args = json.loads(agent_args)
agent_args = agent_args if 'opt_type' in agent_args[
'inference_optimizer_args'] else None
opt_agent = create_agent(env, agent_args=agent_args)[0]
# load the checkpoint
load_checkpoint(opt_agent, opt_exp_key)
agent.inference_optimizer = opt_agent.inference_optimizer
agent.inference_optimizer.n_inf_iters = 10
else:
# create an iterative amortized optimizer
n_input = 12
if ENCODING_TYPE == 'grads':
inputs = ['params', 'grads']
n_input += 12
elif ENCODING_TYPE == 'errors':
inputs = ['params', 'errors']
n_input += (17 + 17 + 6)
n_units = 512
# network_args = {'type': 'fully_connected',
# 'n_layers': 2,
# 'inputs': inputs,
# 'n_units': n_units,
# 'connectivity': 'highway',
# 'batch_norm': False,
# 'non_linearity': 'elu',
# 'dropout': None,
# 'separate_networks': False,
# 'n_input': n_input}
network_args = {
'type': 'recurrent',
'n_layers': 2,
'inputs': inputs,
'n_units': n_units,
'connectivity': 'highway',
'batch_norm': False,
'dropout': None,
'separate_networks': False,
'n_input': n_input
}
agent.inference_optimizer = IterativeInferenceModel(
network_args=network_args,
n_inf_iters=5,
encoding_type=ENCODING_TYPE)
for m in agent.approx_post.models:
agent.approx_post.models[m] = FullyConnectedLayer(n_units, 6)
agent.approx_post.gates[m] = FullyConnectedLayer(
n_units, 6, non_linearity='sigmoid')
# create a parameter optimizer for the inference model
inference_parameters = [_ for _ in agent.inference_optimizer.parameters()
] + [_ for _ in agent.approx_post.parameters()]
param_opt = optim.Adam(inference_parameters, lr=3e-4)
# swap out the value estimator for goal-based estimator
gb_estimator = GoalBasedQEstimator()
# copy over the dynamics model
gb_estimator.state_likelihood_model = agent.q_value_estimator.state_likelihood_model
gb_estimator.state_variable = agent.q_value_estimator.state_variable
# set the estimator
agent.q_value_estimator = gb_estimator
agent.q_value_estimator.set_goal_std(GOAL_STD)
# agent.alphas['pi'] = 0.
model_param_opt = None
if train_model:
# create a parameter optimizer for the inference model
model_parameters = [
_ for _ in
agent.q_value_estimator.state_likelihood_model.parameters()
] + [_ for _ in agent.q_value_estimator.state_variable.parameters()]
model_param_opt = optim.Adam(model_parameters, lr=3e-4)
# optimize goal states
goal_states = []
traj_states = []
env_states = {'qpos': [], 'qvel': []}
actions = []
inf_objectives = []
state_log_likelihoods = []
state_squared_errors = []
state_locs = []
state_scales = []
model_cll_training = []
agent.reset(batch_size=BATCH_SIZE)
agent.eval()
state = sync_env.reset()
if RENDER:
env.render()
goal_state = None
state_likelihood = None
# goal_ind = 0
print('Collecting goal-optimization episode...')
for step_ind in range(N_TOTAL_STEPS):
print('STEP: ' + str(step_ind))
# if step_ind % GOAL_INTERVAL == 0:
if True:
new_goal_states = np.stack([
episode_states[np.random.randint(0, 25)]
for _ in range(BATCH_SIZE)
])
# goal_state = episode_states[goal_ind]
new_goal_states = torch.from_numpy(new_goal_states).float().view(
BATCH_SIZE, -1)
new_goal_states[:, 8:] *= 0.
if step_ind == 0:
goal_state = new_goal_states
else:
# randomly change the goal state with some small probability
flips = (torch.rand(BATCH_SIZE, 1) <
GOAL_FLIP_PROB).float().repeat(
1, new_goal_states.shape[-1])
goal_state = (1 - flips) * goal_state + flips * new_goal_states
if not TRAJECTORY_FOLLOW:
agent.q_value_estimator.set_goal_state(goal_state)
# goal_ind += 1
if TRAJECTORY_FOLLOW:
# define a sub-goal between current state and goal state
delta_state = goal_state - state
traj_state = state + 0.1 * delta_state
agent.q_value_estimator.set_goal_state(traj_state)
traj_states.append(traj_state)
else:
traj_states.append(goal_states)
goal_states.append(goal_state)
qpos = np.stack(
[copy.deepcopy(e.sim.data.qpos) for e in sync_env.envs])
qvel = np.stack(
[copy.deepcopy(e.sim.data.qvel) for e in sync_env.envs])
env_states['qpos'].append(qpos)
env_states['qvel'].append(qvel)
action = agent.act(state, eval=True)
state, _, _, _ = sync_env.step(action)
inf_objectives.append(agent.inference_optimizer.estimated_objectives)
if train_model:
agent.q_value_estimator.generate(agent)
cll = -agent.q_value_estimator.state_variable.cond_log_likelihood(
state).view(-1, 1).mean()
model_cll_training.append(cll.detach().item())
cll.backward()
model_param_opt.step()
if state_likelihood is not None:
state_ll = state_likelihood.log_prob(state)
state_log_likelihoods.append(state_ll)
state_squared_error = (state_likelihood.loc - state).pow(2)
state_squared_errors.append(state_squared_error)
state_loc = agent.collector.distributions['state']['cond_like']['loc'][
-1]
state_scale = agent.collector.distributions['state']['cond_like'][
'scale'][-1]
state_locs.append(state_loc)
state_scales.append(state_scale)
state_likelihood = Normal(state_loc, state_scale)
# update the inference optimizer
grads = [param.grad for param in inference_parameters]
divide_gradients_by_value(grads, agent.inference_optimizer.n_inf_iters)
divide_gradients_by_value(grads, BATCH_SIZE)
param_opt.step()
param_opt.zero_grad()
agent.inference_optimizer.reset(BATCH_SIZE)
if RENDER:
env.render()
actions.append(action)
print('Done.')
# save the results
results = {
'goal_states': goal_states,
'traj_states': traj_states,
'env_states': env_states,
'actions': actions,
'inf_objectives': inf_objectives,
'state_locs': state_locs,
'state_scales': state_scales,
'state_log_likelihoods': state_log_likelihoods,
'state_squared_errors': state_squared_errors,
'model_cll_training': model_cll_training
}
if write_results:
pickle.dump(results, open('goal_opt_' + model_exp_key + '.p', 'wb'))
return results
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, help='environment name')
parser.add_argument('--device_id',
default=None,
type=int,
help='GPU ID number')
parser.add_argument('--checkpoint_exp_key',
default=None,
type=str,
help='experiment key for the checkpoint to load')
args = parser.parse_args()
# create the environment and agent, load checkpoint
env = create_env(args.env, None)
agent, agent_args = create_agent(env, args.device_id)
if args.checkpoint_exp_key is not None:
load_checkpoint(agent, args.checkpoint_exp_key)
pred_horizon = 15
episode, predictions, log_likelihoods = test_model(agent,
env,
horizon=pred_horizon)
# plot_log_likelihoods(torch.stack(log_likelihoods['state'][:975]).view(975,-1))
# plot_log_likelihoods(torch.stack(log_likelihoods['reward'][:975]).view(975,-1))
time_step = 15
# plot_predictions(predictions['state'][time_step], episode['state'][time_step+1:time_step+1+pred_horizon])
import ipdb
ipdb.set_trace()
def evaluate_estimator(exp_key, n_state_action, n_mc_samples, device_id=None):
"""
Evaluates the value estimator of a cached experiment throughout learning.
Args:
exp_key (str): the string of the comet experiment key
n_state_action (int): number of state action pairs to evaluate
n_mc_samples (int): number of Monte Carlo samples to estimate
environment returns
Returns dictionary containing:
ckpt_timesteps [n_ckpts]
value_estimates [n_ckpts, n_state_action, 1],
direct_value_estimates [n_ckpts, n_state_action, 1]
mc_estimates [n_ckpts, n_state_action, n_mc_samples]
"""
# load the experiment
comet_api = comet_ml.API(api_key=LOADING_API_KEY)
experiment = comet_api.get_experiment(project_name=PROJECT_NAME,
workspace=WORKSPACE,
experiment=exp_key)
# create the corresponding environment
param_summary = experiment.get_parameters_summary()
env_name = [a for a in param_summary
if a['name'] == 'env'][0]['valueCurrent']
env = create_env(env_name)
# collect state-action samples using random policy
print('Collecting ' + str(n_state_action) + ' state-action pairs...')
sa_pairs = {'states': [], 'env_states': [], 'actions': []}
state = env.reset()
env_state = (copy.deepcopy(env.sim.data.qpos),
copy.deepcopy(env.sim.data.qvel))
for _ in range(n_state_action):
action = env.action_space.sample()
next_state, reward, done, _ = env.step(action)
sa_pairs['states'].append(state)
sa_pairs['env_states'].append(env_state)
sa_pairs['actions'].append(torch.from_numpy(action).view(1, -1))
state = env.reset() if done else next_state
env_state = (copy.deepcopy(env.sim.data.qpos),
copy.deepcopy(env.sim.data.qvel))
print('Done.')
# enumerate state-action pairs, estimating returns at each stage of learning
asset_list = experiment.get_asset_list()
agent_config_asset_list = [
a for a in asset_list if 'agent_args' in a['fileName']
]
agent_args = None
if len(agent_config_asset_list) > 0:
# if we've saved the agent config dict, load it
agent_args = experiment.get_asset(
agent_config_asset_list[0]['assetId'])
agent_args = json.loads(agent_args)
agent_args = agent_args if 'opt_type' in agent_args[
'inference_optimizer_args'] else None
agent = create_agent(env, agent_args=agent_args, device_id=device_id)[0]
# get the list of checkpoint timesteps
ckpt_asset_list = [a for a in asset_list if 'ckpt' in a['fileName']]
ckpt_asset_names = [a['fileName'] for a in ckpt_asset_list]
ckpt_timesteps = [
int(s.split('ckpt_step_')[1].split('.ckpt')[0])
for s in ckpt_asset_names
]
# convert n_mc_samples to a round number of batches
n_batches = math.ceil(n_mc_samples / ROLLOUT_BATCH_SIZE)
n_mc_samples = ROLLOUT_BATCH_SIZE * n_batches
# TODO: the first dimension should be divided by CKPT_SUBSAMPLE
value_estimates = np.zeros((len(ckpt_timesteps), n_state_action, 1))
direct_value_estimates = np.zeros((len(ckpt_timesteps), n_state_action, 1))
mc_estimates = np.zeros(
(len(ckpt_timesteps), n_state_action, n_mc_samples))
# iterate over sub-sampled checkpoint timesteps, evaluating
ckpt_timesteps = list(np.sort(ckpt_timesteps)[::CKPT_SUBSAMPLE])
for ckpt_ind, ckpt_timestep in enumerate(ckpt_timesteps):
# load the checkpoint
print('Evaluating checkpoint ' + str(ckpt_ind + 1) + ' of ' +
str(len(ckpt_timesteps)))
load_checkpoint(agent, exp_key, ckpt_timestep)
# get value estimate and estimate returns for the state-action pairs
for sa_ind, (env_state, state, act) in enumerate(
zip(sa_pairs['env_states'], sa_pairs['states'],
sa_pairs['actions'])):
t_start = time.time()
action_value_estimate = get_agent_value_estimate(agent, state, act)
value_estimates[ckpt_ind,
sa_ind, :] = action_value_estimate['estimate']
direct_value_estimates[ckpt_ind,
sa_ind, :] = action_value_estimate['direct']
returns = estimate_monte_carlo_return(env, agent, env_state, state,
act, n_batches)
mc_estimates[ckpt_ind, sa_ind, :] = returns
if sa_ind % 1 == 0:
print(' Evaluated ' + str(sa_ind + 1) + ' of ' +
str(len(sa_pairs['states'])) + ' state-action pairs.')
print(' Duration: ' + '{:.2f}'.format(time.time() - t_start) +
' s / state-action pair.')
# TODO: log the value estimates to comet (need to json-ify the numpy arrays)
# prev_exp = comet_ml.ExistingExperiment(api_key=LOGGING_API_KEY,
# previous_experiment=exp_key)
# prev_exp.log_asset_data(value_estimates, name='value_estimates')
# prev_exp.log_asset_data(direct_value_estimates, name='direct_value_estimates')
# prev_exp.log_asset_data(mc_estimates, name='mc_estimates')
return {
'ckpt_timesteps': ckpt_timesteps,
'value_estimates': value_estimates,
'direct_value_estimates': direct_value_estimates,
'mc_estimates': mc_estimates
}