@chinen93 agent = Agent.create(
agent='tensorforce',
environment=environment, # alternatively: states, actions, (max_episode_timesteps)
memory=10000,
update=dict(unit='timesteps', batch_size=64),
optimizer=dict(type='adam', learning_rate=3e-4),
policy=dict(network='auto'),
objective='policy_gradient',
reward_estimation=dict(horizon=20)
) - can you please guide me into how I can add the entropy regularization and exploration? The agent starts consistently taking the same action after few episodes - I think it might be prematurely converging to optimal policy when more learning needs to be done
agent='tensorforce',
environment=environment, # alternatively: states, actions, (max_episode_timesteps)
memory=10000,
update=dict(unit='timesteps', batch_size=64),
optimizer=dict(type='adam', learning_rate=3e-4),
policy=dict(network='auto'),
objective='policy_gradient',
reward_estimation=dict(horizon=20)
) - can you please guide me into how I can add the entropy regularization and exploration? The agent starts consistently taking the same action after few episodes - I think it might be prematurely converging to optimal policy when more learning needs to be done
Take a look in this doc link about the "tensorforce" agente: https://tensorforce.readthedocs.io/en/latest/agents/tensorforce.html
You can add entropy_regularization, l2_regularization, exploration and other hyperparameters to try to improve your agent. However it depends on your action space
@nasrashvilg1 so try to create your agent as:
Agent.create(
agent='tensorforce',
environment=environment, # alternatively: states, actions, (max_episode_timesteps)
memory=10000,
update=dict(unit='timesteps', batch_size=64),
optimizer=dict(type='adam', learning_rate=3e-4),
policy=dict(network='auto'),
objective='policy_gradient',
reward_estimation=dict(horizon=20),
exploration=3e-4,
entropy_regularization=1e-4,
l2_regularization=1e-4
)
And change the last configurations as needed.
Agent.create(
agent='tensorforce',
environment=environment, # alternatively: states, actions, (max_episode_timesteps)
memory=10000,
update=dict(unit='timesteps', batch_size=64),
optimizer=dict(type='adam', learning_rate=3e-4),
policy=dict(network='auto'),
objective='policy_gradient',
reward_estimation=dict(horizon=20),
exploration=3e-4,
entropy_regularization=1e-4,
l2_regularization=1e-4
)
And change the last configurations as needed.
in the last configuration you mean exploraiton, entropy_regularization and l2_regularization
You can either use the pretrain function (if you have data in the right format, see in the example), or more manually use the experience and update functions. A supervised learning environment probably won't work as well.
@GANdalf2357, you can use the https://github.com/tensorforce/tensorforce/blob/master/examples/save_load_agent.py to see some examples of how to save/load a model. The runner part can be explicit with a while-loop if you need more control.
Hi @GANdalf2357, in addition to what @chinen93 said: inference-only can be done e.g. by using
evaluation=True when using Runner, or act with independent=True and deterministic=True (see here).
@chinen93, the combination of experience and update is a bit like supervised learning, so train the policy distribution to output the corresponding action per state according to the data. This consideration is more or less ignoring the theory around policy gradient and on-policy etc, just looking at the problem from a supervised angle, and this can work (but can also not lead anywhere). The current Tensorforce interface is not ideal, bit too generic and hence may be used wrongly, but I also don't have too much experience with pretraining, behavioral cloning, etc.
The pretrain function is a bit specific and assumes interaction traces including reward, so basically the recorded data of another agent, as in the pretrain example. Experience and update gives more flexibility, but might not be obvious. What data do you have? Individual data points of "expert" state->action decisions? Demonstration traces of state-action pairs? Or full demo traces including reward? Or potentially even more "random" trajectory data, not "expert"/"demonstration"? Depending on which case, applies, there are different possibilities.
is there a lot of value in having discretized space for PPO agent or any other agent in general? I have a continues state space with 5 readings - which can range from 0.0 to 50. 0(float numbers) I can also have all the 5 readings discretized - just trying to decide if it's worth the effort?
Hi all, I'm having a problem with nested tensor specifications. I specified a nested block called "MID_1_bid_0", with a float element "qty", but when I try to run the network I get this error:
ValueError: 'MID_1_bid_0/qty_preprocessing' is not a valid module name. Module names must be valid Python identifiers (e.g. a valid class name).
This is in the state space of the agent.
From looking at the source it appears that the naming convention is indeed to separate parent and child objects with a '/', but then that name containing a '/' gets passed as a module name to tf.Module, which is illegal.
I'm getting this warning when I start a runner, and it's taking 2-3 minutes for the preprocessing to complete before running. Is that normal, and is there a known misuse that would cause this?
/site-packages/tensorflow/python/framework/indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
@AlexKuhnle I think I have another bug for you. In Agent.py, the first thing fn_act() does is to separate any variable
name + '_mask' from the state spec where name is any name found in the action_spec. It does this recursively using fmap(), calling this function: # Separate auxiliaries
def function(name, spec):
auxiliary = ArrayDict()
if self.config.enable_int_action_masking and spec.type == 'int' and \
spec.num_values is not None:
if name is None:
name = 'action'
# Mask, either part of states or default all true
auxiliary['mask'] = states.pop(name + '_mask', np.ones(
shape=(num_parallel,) + spec.shape + (spec.num_values,), dtype=spec.np_type()
))
return auxiliaryHowever, when I run it I get a KeyError exception, where the key is a root name from action space. I instrumented the line from nested_dict.pop() that threw the error, like so:
3 elif '/' in key:
2 key, subkey = key.split('/', 1)
1 if not key in self:
371 print(f"pop {key} {subkey}")
1 import pprint
2 pprint.pprint(self)
3 value = super().__getitem__(key)
4 assert isinstance(value, self.__class__)
5 return value.pop(subkey, default)This is what it printed:pop MID_1_counter_0 promise_date_mask
{'MID_1_bid_0/price': array([1.15]),
'MID_1_bid_0/promise_date': array([4]),
'MID_1_bid_0/qty': array([44135.2]),
'MID_1_bid_0/supplier_tier': array([0]),
...
Now, in the top level to pop() there is a default value, however, when a nested variable is encountered, the handling for that case doesn't consult the default value, it just says
value = super().__getitem__(key)
Hey, first, the warning
Converting sparse IndexedSlices to a dense Tensor of unknown shape. comes up if you use embeddings (used by "auto" network if state is int), and maybe in other situations as well. I've read a bit about it a while ago, and it doesn't seem to be critical, if e.g. the number of embeddings (num_values of int state) is reasonable. Model initialization may take a while if the network is bigger -- is this the case for you?
And regarding the second issue: I will look into it soon.
@AlexKuhnle Thanks for the tip. Meanwhile, I modified the pop() code in nested_dict to return the default if the super key is not found, (instead of printing debug info), but it led to an invalid-shape error. I think the problem is that if the action is nested then there won't be a shape argument for parent nodes (only leaf nodes have a shape). Given that, what I would like to do is to set
self.config.enable_int_action_masking to False, but I don't see a way to do that... The config object explicitly overrides __set_attr__ and I wasn't able to pass it as a constructor arg to the agent. So, what's the right way to do that?
Setting
enable_int_action_masking can be done via the config argument of any agent (docs here). That should hopefully work.
I've also made the change you suggested to
NestedDict -- would you mind posting the shape exception, since I don't know why that would come up?
Here is the stack trace:
File "train_rl_agent.py", line 77, in run_agent
runner.run(num_episodes=sim_config["train_episodes"])
File "/home/hinrichs/build/tensorforce/tensorforce/execution/runner.py", line 545, in run
self.handle_act(parallel=n)
File "/home/hinrichs/build/tensorforce/tensorforce/execution/runner.py", line 579, in handle_act
actions = self.agent.act(states=self.states[parallel], parallel=parallel)
File "/home/hinrichs/build/tensorforce/tensorforce/agents/agent.py", line 388, in act
deterministic=deterministic
File "/home/hinrichs/build/tensorforce/tensorforce/agents/recorder.py", line 267, in act
num_parallel=num_parallel
File "/home/hinrichs/build/tensorforce/tensorforce/agents/agent.py", line 415, in fn_act
states = self.states_spec.to_tensor(value=states, batched=True, name='Agent.act states')
File "/home/hinrichs/build/tensorforce/tensorforce/core/utils/tensors_spec.py", line 57, in to_tensor
value=value[name], batched=batched, recover_empty=recover_empty
File "/home/hinrichs/build/tensorforce/tensorforce/core/utils/tensors_spec.py", line 57, in to_tensor
value=value[name], batched=batched, recover_empty=recover_empty
File "/home/hinrichs/build/tensorforce/tensorforce/core/utils/tensor_spec.py", line 149, in to_tensor
raise TensorforceError.value(name=name, argument='value', value=value, hint='shape')
tensorforce.exception.TensorforceError: Invalid value for TensorSpec.to_tensor argument value: 0 shape.@AlexKuhnle I've figured out what's happening, but I don't fully understand the cause. I instrumented the code where the exception is raised like so:
3 # Check whether shape matches
2 if value.shape[int(batched):] != self.shape:
1 print(f"\nvalue {value} type {type(value)} batched {batched}")
150 print(f"value shape {value.shape[int(batched):]} self shape {self.shape}")
1 import pprint
2 pprint.pprint(self)
3 pprint.pprint(value)
4 raise TensorforceError.value(name=name, argument='value', value=value, hint='shape')and this is what it prints:
value [0] type <class 'numpy.ndarray'>
value shape () self shape (1,)
TensorSpec(type=int, shape=(1,), num_values=4)
array([0])The reason is that batched is True, but the value shape doesn't have a batch dimension.
I realise there is something missing which makes the exception message less useful/specific -- will fix that. But it looks like a subtle shape problem of some inputs, as if the value returned by the environment is not perfectly matching the shape of the states specification.
Ah, was just writing :-)
I was just writing too - I tried removing
batch_size from the agent params, but it tells me that one is required.
Tensorforce is very strict about these shapes, since TensorFlow and the computation graph are, too (but unlike e.g. NumPy, which is often very forgiving).
I put in a shape of (1,) for all scalars earlier to remove sources of variation while debugging another problem. I can try it now without that.