Writing your environment and transforms with TorchRL — torchrl main documentation (2024)

Note

Go to the endto download the full example code

Creating an environment (a simulator or an interface to a physical control system)is an integrative part of reinforcement learning and control engineering.

TorchRL provides a set of tools to do this in multiple contexts.This tutorial demonstrates how to use PyTorch and TorchRL code a pendulumsimulator from the ground up.It is freely inspired by the Pendulum-v1 implementation from OpenAI-Gym/Farama-Gymnasiumcontrol library.

Writing your environment and transforms with TorchRL — torchrl main documentation (1)

Key learnings:

How to design an environment in TorchRL:- Writing specs (input, observation and reward);- Implementing behavior: seeding, reset and step.
Transforming your environment inputs and outputs, and writing your owntransforms;
How to use TensorDict to carry arbitrary data structuresthrough the codebase.
In the process, we will touch three crucial components of TorchRL:

environments
transforms
models (policy and value function)

To give a sense of what can be achieved with TorchRL’s environments, we willbe designing a stateless environment. While stateful environments keep track ofthe latest physical state encountered and rely on this to simulate the state-to-statetransition, stateless environments expect the current state to be provided tothem at each step, along with the action undertaken. TorchRL supports bothtypes of environments, but stateless environments are more generic and hencecover a broader range of features of the environment API in TorchRL.

Modeling stateless environments gives users full control over the input andoutputs of the simulator: one can reset an experiment at any stage or activelymodify the dynamics from the outside. However, it assumes that we have some controlover a task, which may not always be the case: solving a problem where we cannotcontrol the current state is more challenging but has a much wider set of applications.

Another advantage of stateless environments is that they can enablebatched execution of transition simulations. If the backend and theimplementation allow it, an algebraic operation can be executed seamlessly onscalars, vectors, or tensors. This tutorial gives such examples.

This tutorial will be structured as follows:

We will first get acquainted with the environment properties:its shape (batch_size), its methods (mainly step(),reset() and set_seed())and finally its specs.
After having coded our simulator, we will demonstrate how it can be usedduring training with transforms.
We will explore new avenues that follow from the TorchRL’s API,including: the possibility of transforming inputs, the vectorized executionof the simulation and the possibility of backpropagation through thesimulation graph.
Finally, we will train a simple policy to solve the system we implemented.

from collections import defaultdictfrom typing import Optionalimport numpy as npimport torchimport tqdmfrom tensordict import TensorDict, TensorDictBasefrom tensordict.nn import TensorDictModulefrom torch import nnfrom torchrl.data import BoundedTensorSpec, CompositeSpec, UnboundedContinuousTensorSpecfrom torchrl.envs import ( CatTensors, EnvBase, Transform, TransformedEnv, UnsqueezeTransform,)from torchrl.envs.transforms.transforms import _apply_to_compositefrom torchrl.envs.utils import check_env_specs, step_mdpDEFAULT_X = np.piDEFAULT_Y = 1.0

There are four things you must take care of when designing a new environmentclass:

EnvBase._reset(), which codes for the resetting of the simulatorat a (potentially random) initial state;
EnvBase._step() which codes for the state transition dynamic;
EnvBase._set_seed`() which implements the seeding mechanism;
the environment specs.

Let us first describe the problem at hand: we would like to model a simplependulum over which we can control the torque applied on its fixed point.Our goal is to place the pendulum in upward position (angular position at 0by convention) and having it standing still in that position.To design our dynamic system, we need to define two equations: the motionequation following an action (the torque applied) and the reward equationthat will constitute our objective function.

For the motion equation, we will update the angular velocity following:

\[\dot{\theta}_{t+1} = \dot{\theta}_t + (3 * g / (2 * L) * \sin(\theta_t) + 3 / (m * L^2) * u) * dt\]

where \(\dot{\theta}\) is the angular velocity in rad/sec, \(g\) is thegravitational force, \(L\) is the pendulum length, \(m\) is its mass,\(\theta\) is its angular position and \(u\) is the torque. Theangular position is then updated according to

\[\theta_{t+1} = \theta_{t} + \dot{\theta}_{t+1} dt\]

We define our reward as

\[r = -(\theta^2 + 0.1 * \dot{\theta}^2 + 0.001 * u^2)\]

which will be maximized when the angle is close to 0 (pendulum in upwardposition), the angular velocity is close to 0 (no motion) and the torque is0 too.

Coding the effect of an action: `_step()`¶

The step method is the first thing to consider, as it will encodethe simulation that is of interest to us. In TorchRL, theEnvBase class has a EnvBase.step()method that receives a tensordict.TensorDictinstance with an "action" entry indicating what action is to be taken.

To facilitate the reading and writing from that tensordict and to make surethat the keys are consistent with what’s expected from the library, thesimulation part has been delegated to a private abstract method _step()which reads input data from a tensordict, and writes a new tensordictwith the output data.

The _step() method should do the following:

Read the input keys (such as "action") and execute the simulationbased on these;
Retrieve observations, done state and reward;
Write the set of observation values along with the reward and done stateat the corresponding entries in a new TensorDict.

Next, the step() method will merge the outputof step() in the input tensordict to enforceinput/output consistency.

Typically, for stateful environments, this will look like this:

Resetting the simulator: `_reset()`¶

The second method we need to care about is the_reset() method. Like_step(), it should write the observation entriesand possibly a done state in the tensordict it outputs (if the done state isomitted, it will be filled as False by the parent methodreset()). In some contexts, it is required thatthe _reset method receives a command from the function that calledit (for example, in multi-agent settings we may want to indicate which agents needto be reset). This is why the _reset() methodalso expects a tensordict as input, albeit it may perfectly be empty orNone.

The parent EnvBase.reset() does some simple checks like theEnvBase.step() does, such as making sure that a "done" stateis returned in the output tensordict and that the shapes match what isexpected from the specs.

For us, the only important thing to consider is whetherEnvBase._reset() contains all the expected observations. Once more,since we are working with a stateless environment, we pass the configurationof the pendulum in a nested tensordict named "params".

In this example, we do not pass a done state as this is not mandatoryfor _reset() and our environment is non-terminating, so we alwaysexpect it to be False.

def _reset(self, tensordict): if tensordict is None or tensordict.is_empty(): # if no ``tensordict`` is passed, we generate a single set of hyperparameters # Otherwise, we assume that the input ``tensordict`` contains all the relevant # parameters to get started. tensordict = self.gen_params(batch_size=self.batch_size) high_th = torch.tensor(DEFAULT_X, device=self.device) high_thdot = torch.tensor(DEFAULT_Y, device=self.device) low_th = -high_th low_thdot = -high_thdot # for non batch-locked environments, the input ``tensordict`` shape dictates the number # of simulators run simultaneously. In other contexts, the initial # random state's shape will depend upon the environment batch-size instead. th = ( torch.rand(tensordict.shape, generator=self.rng, device=self.device) * (high_th - low_th) + low_th ) thdot = ( torch.rand(tensordict.shape, generator=self.rng, device=self.device) * (high_thdot - low_thdot) + low_thdot ) out = TensorDict( { "th": th, "thdot": thdot, "params": tensordict["params"], }, batch_size=tensordict.shape, ) return out

Environment metadata: `env.*_spec`¶

The specs define the input and output domain of the environment.It is important that the specs accurately define the tensors that will bereceived at runtime, as they are often used to carry information aboutenvironments in multiprocessing and distributed settings. They can also beused to instantiate lazily defined neural networks and test scripts withoutactually querying the environment (which can be costly with real-worldphysical systems for instance).

There are four specs that we must code in our environment:

EnvBase.observation_spec: This will be a CompositeSpecinstance where each key is an observation (a CompositeSpec can beviewed as a dictionary of specs).
EnvBase.action_spec: It can be any type of spec, but it is requiredthat it corresponds to the "action" entry in the input tensordict;
EnvBase.reward_spec: provides information about the reward space;
EnvBase.done_spec: provides information about the space of the doneflag.

TorchRL specs are organized in two general containers: input_spec whichcontains the specs of the information that the step function reads (dividedbetween action_spec containing the action and state_spec containingall the rest), and output_spec which encodes the specs that thestep outputs (observation_spec, reward_spec and done_spec).In general, you should not interact directly with output_spec andinput_spec but only with their content: observation_spec,reward_spec, done_spec, action_spec and state_spec.The reason if that the specs are organized in a non-trivial waywithin output_spec andinput_spec and neither of these should be directly modified.

In other words, the observation_spec and related properties areconvenient shortcuts to the content of the output and input spec containers.

TorchRL offers multiple TensorSpecsubclasses toencode the environment’s input and output characteristics.

Specs shape¶

The environment specs leading dimensions must match theenvironment batch-size. This is done to enforce that every component of anenvironment (including its transforms) have an accurate representation ofthe expected input and output shapes. This is something that should beaccurately coded in stateful settings.

For non batch-locked environments, such as the one in our example (see below),this is irrelevant as the environment batch size will most likely be empty.

def _make_spec(self, td_params): # Under the hood, this will populate self.output_spec["observation"] self.observation_spec = CompositeSpec( th=BoundedTensorSpec( low=-torch.pi, high=torch.pi, shape=(), dtype=torch.float32, ), thdot=BoundedTensorSpec( low=-td_params["params", "max_speed"], high=td_params["params", "max_speed"], shape=(), dtype=torch.float32, ), # we need to add the ``params`` to the observation specs, as we want # to pass it at each step during a rollout params=make_composite_from_td(td_params["params"]), shape=(), ) # since the environment is stateless, we expect the previous output as input. # For this, ``EnvBase`` expects some state_spec to be available self.state_spec = self.observation_spec.clone() # action-spec will be automatically wrapped in input_spec when # `self.action_spec = spec` will be called supported self.action_spec = BoundedTensorSpec( low=-td_params["params", "max_torque"], high=td_params["params", "max_torque"], shape=(1,), dtype=torch.float32, ) self.reward_spec = UnboundedContinuousTensorSpec(shape=(*td_params.shape, 1))def make_composite_from_td(td): # custom function to convert a ``tensordict`` in a similar spec structure # of unbounded values. composite = CompositeSpec( { key: make_composite_from_td(tensor) if isinstance(tensor, TensorDictBase) else UnboundedContinuousTensorSpec( dtype=tensor.dtype, device=tensor.device, shape=tensor.shape ) for key, tensor in td.items() }, shape=td.shape, ) return composite

Reproducible experiments: seeding¶

Seeding an environment is a common operation when initializing an experiment.The only goal of EnvBase._set_seed() is to set the seed of the containedsimulator. If possible, this operation should not call reset() or interactwith the environment execution. The parent EnvBase.set_seed() methodincorporates a mechanism that allows seeding multiple environments with adifferent pseudo-random and reproducible seed.

def _set_seed(self, seed: Optional[int]): rng = torch.manual_seed(seed) self.rng = rng

Wrapping things together: the EnvBase class¶

We can finally put together the pieces and design our environment class.The specs initialization needs to be performed during the environmentconstruction, so we must take care of calling the _make_spec() methodwithin PendulumEnv.__init__().

We add a static method PendulumEnv.gen_params() which deterministicallygenerates a set of hyperparameters to be used during execution:

def gen_params(g=10.0, batch_size=None) -> TensorDictBase: """Returns a ``tensordict`` containing the physical parameters such as gravitational force and torque or speed limits.""" if batch_size is None: batch_size = [] td = TensorDict( { "params": TensorDict( { "max_speed": 8, "max_torque": 2.0, "dt": 0.05, "g": g, "m": 1.0, "l": 1.0, }, [], ) }, [], ) if batch_size: td = td.expand(batch_size).contiguous() return td

We define the environment as non-batch_locked by turning the hom*onymousattribute to False. This means that we will not enforce the inputtensordict to have a batch-size that matches the one of the environment.

The following code will just put together the pieces we have coded above.

class PendulumEnv(EnvBase): metadata = { "render_modes": ["human", "rgb_array"], "render_fps": 30, } batch_locked = False def __init__(self, td_params=None, seed=None, device="cpu"): if td_params is None: td_params = self.gen_params() super().__init__(device=device, batch_size=[]) self._make_spec(td_params) if seed is None: seed = torch.empty((), dtype=torch.int64).random_().item() self.set_seed(seed) # Helpers: _make_step and gen_params gen_params = staticmethod(gen_params) _make_spec = _make_spec # Mandatory methods: _step, _reset and _set_seed _reset = _reset _step = staticmethod(_step) _set_seed = _set_seed

Testing our environment¶

TorchRL provides a simple function check_env_specs()to check that a (transformed) environment has an input/output structure thatmatches the one dictated by its specs.Let us try it out:

env = PendulumEnv()check_env_specs(env)

We can have a look at our specs to have a visual representation of the environmentsignature:

print("observation_spec:", env.observation_spec)print("state_spec:", env.state_spec)print("reward_spec:", env.reward_spec)

observation_spec: CompositeSpec( th: BoundedTensorSpec( shape=torch.Size([]), space=ContinuousBox( low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), thdot: BoundedTensorSpec( shape=torch.Size([]), space=ContinuousBox( low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), params: CompositeSpec( max_speed: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.int64, domain=discrete), max_torque: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.float32, domain=continuous), dt: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.float32, domain=continuous), g: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.float32, domain=continuous), m: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.float32, domain=continuous), l: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([]))state_spec: CompositeSpec( th: BoundedTensorSpec( shape=torch.Size([]), space=ContinuousBox( low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), thdot: BoundedTensorSpec( shape=torch.Size([]), space=ContinuousBox( low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous), params: CompositeSpec( max_speed: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.int64, domain=discrete), max_torque: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.float32, domain=continuous), dt: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.float32, domain=continuous), g: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.float32, domain=continuous), m: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.float32, domain=continuous), l: UnboundedContinuousTensorSpec( shape=torch.Size([]), space=None, device=cpu, dtype=torch.float32, domain=continuous), device=cpu, shape=torch.Size([])), device=cpu, shape=torch.Size([]))reward_spec: UnboundedContinuousTensorSpec( shape=torch.Size([1]), space=ContinuousBox( low=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True), high=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True)), device=cpu, dtype=torch.float32, domain=continuous)

We can execute a couple of commands too to check that the output structurematches what is expected.

td = env.reset()print("reset tensordict", td)

reset tensordict TensorDict( fields={ done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), params: TensorDict( fields={ dt: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), g: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), l: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), m: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), max_speed: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False), max_torque: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([]), device=None, is_shared=False), terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), th: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), thdot: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([]), device=None, is_shared=False)

We can run the env.rand_step() to generatean action randomly from the action_spec domain. A tensordict containingthe hyperparameters and the current state must be passed since ourenvironment is stateless. In stateful contexts, env.rand_step() worksperfectly too.

td = env.rand_step(td)print("random step tensordict", td)

random step tensordict TensorDict( fields={ action: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), next: TensorDict( fields={ done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), params: TensorDict( fields={ dt: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), g: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), l: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), m: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), max_speed: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False), max_torque: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([]), device=None, is_shared=False), reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), th: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), thdot: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([]), device=None, is_shared=False), params: TensorDict( fields={ dt: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), g: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), l: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), m: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), max_speed: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False), max_torque: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([]), device=None, is_shared=False), terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False), th: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), thdot: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([]), device=None, is_shared=False)

Transforming an environment¶

Writing environment transforms for stateless simulators is slightly morecomplicated than for stateful ones: transforming an output entry that needsto be read at the following iteration requires to apply the inverse transformbefore calling meth.step() at the next step.This is an ideal scenario to showcase all the features of TorchRL’stransforms!

For instance, in the following transformed environment we unsqueeze the entries["th", "thdot"] to be able to stack them along the lastdimension. We also pass them as in_keys_inv to squeeze them back to theiroriginal shape once they are passed as input in the next iteration.

env = TransformedEnv( env, # ``Unsqueeze`` the observations that we will concatenate UnsqueezeTransform( unsqueeze_dim=-1, in_keys=["th", "thdot"], in_keys_inv=["th", "thdot"], ),)

Writing custom transforms¶

TorchRL’s transforms may not cover all the operations one wants to executeafter an environment has been executed.Writing a transform does not require much effort. As for the environmentdesign, there are two steps in writing a transform:

Getting the dynamics right (forward and inverse);
Adapting the environment specs.

A transform can be used in two settings: on its own, it can be used as aModule. It can also be used appended to aTransformedEnv. The structure of the class allows tocustomize the behavior in the different contexts.

A Transform skeleton can be summarized as follows:

class Transform(nn.Module): def forward(self, tensordict): ... def _apply_transform(self, tensordict): ... def _step(self, tensordict): ... def _call(self, tensordict): ... def inv(self, tensordict): ... def _inv_apply_transform(self, tensordict): ...

There are three entry points (forward(), _step() and inv())which all receive tensordict.TensorDict instances. The first twowill eventually go through the keys indicated by in_keysand call _apply_transform() to each of these. The results willbe written in the entries pointed by Transform.out_keys if provided(if not the in_keys will be updated with the transformed values).If inverse transforms need to be executed, a similar data flow will beexecuted but with the Transform.inv() andTransform._inv_apply_transform() methods and across the in_keys_invand out_keys_inv list of keys.The following figure summarized this flow for environments and replaybuffers.

Transform API

In some cases, a transform will not work on a subset of keys in a unitarymanner, but will execute some operation on the parent environment orwork with the entire input tensordict.In those cases, the _call() and forward() methods should bere-written, and the _apply_transform() method can be skipped.

Let us code new transforms that will compute the sine and cosinevalues of the position angle, as these values are more useful to us to learna policy than the raw angle value:

class SinTransform(Transform): def _apply_transform(self, obs: torch.Tensor) -> None: return obs.sin() # The transform must also modify the data at reset time def _reset( self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase ) -> TensorDictBase: return self._call(tensordict_reset) # _apply_to_composite will execute the observation spec transform across all # in_keys/out_keys pairs and write the result in the observation_spec which # is of type ``Composite`` @_apply_to_composite def transform_observation_spec(self, observation_spec): return BoundedTensorSpec( low=-1, high=1, shape=observation_spec.shape, dtype=observation_spec.dtype, device=observation_spec.device, )class CosTransform(Transform): def _apply_transform(self, obs: torch.Tensor) -> None: return obs.cos() # The transform must also modify the data at reset time def _reset( self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase ) -> TensorDictBase: return self._call(tensordict_reset) # _apply_to_composite will execute the observation spec transform across all # in_keys/out_keys pairs and write the result in the observation_spec which # is of type ``Composite`` @_apply_to_composite def transform_observation_spec(self, observation_spec): return BoundedTensorSpec( low=-1, high=1, shape=observation_spec.shape, dtype=observation_spec.dtype, device=observation_spec.device, )t_sin = SinTransform(in_keys=["th"], out_keys=["sin"])t_cos = CosTransform(in_keys=["th"], out_keys=["cos"])env.append_transform(t_sin)env.append_transform(t_cos)

TransformedEnv( env=PendulumEnv(), transform=Compose( UnsqueezeTransform(unsqueeze_dim=-1, in_keys=['th', 'thdot'], out_keys=['th', 'thdot'], in_keys_inv=['th', 'thdot'], out_keys_inv=['th', 'thdot']), SinTransform(keys=['th']), CosTransform(keys=['th'])))

Concatenates the observations onto an “observation” entry.del_keys=False ensures that we keep these values for the nextiteration.

cat_transform = CatTensors( in_keys=["sin", "cos", "thdot"], dim=-1, out_key="observation", del_keys=False)env.append_transform(cat_transform)

TransformedEnv( env=PendulumEnv(), transform=Compose( UnsqueezeTransform(unsqueeze_dim=-1, in_keys=['th', 'thdot'], out_keys=['th', 'thdot'], in_keys_inv=['th', 'thdot'], out_keys_inv=['th', 'thdot']), SinTransform(keys=['th']), CosTransform(keys=['th']), CatTensors(in_keys=['cos', 'sin', 'thdot'], out_key=observation)))

Once more, let us check that our environment specs match what is received:

check_env_specs(env)

Executing a rollout¶

Executing a rollout is a succession of simple steps:

reset the environment
while some condition is not met:
- compute an action given a policy
- execute a step given this action
- collect the data
- make a MDP step
gather the data and return

These operations have been conveniently wrapped in the rollout()method, from which we provide a simplified version here below.

def simple_rollout(steps=100): # preallocate: data = TensorDict({}, [steps]) # reset _data = env.reset() for i in range(steps): _data["action"] = env.action_spec.rand() _data = env.step(_data) data[i] = _data _data = step_mdp(_data, keep_other=True) return dataprint("data from rollout:", simple_rollout(100))

data from rollout: TensorDict( fields={ action: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False), cos: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False), next: TensorDict( fields={ cos: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False), observation: Tensor(shape=torch.Size([100, 3]), device=cpu, dtype=torch.float32, is_shared=False), params: TensorDict( fields={ dt: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False), g: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False), l: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False), m: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False), max_speed: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.int64, is_shared=False), max_torque: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([100]), device=None, is_shared=False), reward: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False), sin: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False), th: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False), thdot: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([100]), device=None, is_shared=False), observation: Tensor(shape=torch.Size([100, 3]), device=cpu, dtype=torch.float32, is_shared=False), params: TensorDict( fields={ dt: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False), g: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False), l: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False), m: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False), max_speed: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.int64, is_shared=False), max_torque: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([100]), device=None, is_shared=False), sin: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False), th: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False), thdot: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([100]), device=None, is_shared=False)

Batching computations¶

The last unexplored end of our tutorial is the ability that we have tobatch computations in TorchRL. Because our environment does notmake any assumptions regarding the input data shape, we can seamlesslyexecute it over batches of data. Even better: for non-batch-lockedenvironments such as our Pendulum, we can change the batch size on the flywithout recreating the environment.To do this, we just generate parameters with the desired shape.

batch_size = 10 # number of environments to be executed in batchtd = env.reset(env.gen_params(batch_size=[batch_size]))print("reset (batch size of 10)", td)td = env.rand_step(td)print("rand step (batch size of 10)", td)

reset (batch size of 10) TensorDict( fields={ cos: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), observation: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False), params: TensorDict( fields={ dt: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), g: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), l: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), m: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), max_speed: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.int64, is_shared=False), max_torque: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([10]), device=None, is_shared=False), sin: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), th: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), thdot: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([10]), device=None, is_shared=False)rand step (batch size of 10) TensorDict( fields={ action: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), cos: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), next: TensorDict( fields={ cos: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), observation: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False), params: TensorDict( fields={ dt: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), g: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), l: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), m: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), max_speed: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.int64, is_shared=False), max_torque: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([10]), device=None, is_shared=False), reward: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), sin: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), th: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), thdot: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([10]), device=None, is_shared=False), observation: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False), params: TensorDict( fields={ dt: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), g: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), l: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), m: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False), max_speed: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.int64, is_shared=False), max_torque: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([10]), device=None, is_shared=False), sin: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False), th: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False), thdot: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([10]), device=None, is_shared=False)

Executing a rollout with a batch of data requires us to reset the environmentout of the rollout function, since we need to define the batch_sizedynamically and this is not supported by rollout():

rollout = env.rollout( 3, auto_reset=False, # we're executing the reset out of the ``rollout`` call tensordict=env.reset(env.gen_params(batch_size=[batch_size])),)print("rollout of len 3 (batch size of 10):", rollout)

rollout of len 3 (batch size of 10): TensorDict( fields={ action: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False), cos: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.bool, is_shared=False), next: TensorDict( fields={ cos: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False), done: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.bool, is_shared=False), observation: Tensor(shape=torch.Size([10, 3, 3]), device=cpu, dtype=torch.float32, is_shared=False), params: TensorDict( fields={ dt: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False), g: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False), l: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False), m: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False), max_speed: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.int64, is_shared=False), max_torque: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([10, 3]), device=None, is_shared=False), reward: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False), sin: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.bool, is_shared=False), th: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False), thdot: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([10, 3]), device=None, is_shared=False), observation: Tensor(shape=torch.Size([10, 3, 3]), device=cpu, dtype=torch.float32, is_shared=False), params: TensorDict( fields={ dt: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False), g: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False), l: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False), m: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False), max_speed: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.int64, is_shared=False), max_torque: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([10, 3]), device=None, is_shared=False), sin: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False), terminated: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.bool, is_shared=False), th: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False), thdot: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([10, 3]), device=None, is_shared=False)

Training a simple policy¶

In this example, we will train a simple policy using the reward as adifferentiable objective, such as a negative loss.We will take advantage of the fact that our dynamic system is fullydifferentiable to backpropagate through the trajectory return and adjust theweights of our policy to maximize this value directly. Of course, in manysettings many of the assumptions we make do not hold, such asdifferentiable system and full access to the underlying mechanics.

Still, this is a very simple example that showcases how a training loop canbe coded with a custom environment in TorchRL.

Let us first write the policy network:

torch.manual_seed(0)env.set_seed(0)net = nn.Sequential( nn.LazyLinear(64), nn.Tanh(), nn.LazyLinear(64), nn.Tanh(), nn.LazyLinear(64), nn.Tanh(), nn.LazyLinear(1),)policy = TensorDictModule( net, in_keys=["observation"], out_keys=["action"],)

and our optimizer:

optim = torch.optim.Adam(policy.parameters(), lr=2e-3)

Training loop¶

We will successively:

generate a trajectory
sum the rewards
backpropagate through the graph defined by these operations
clip the gradient norm and make an optimization step
repeat

At the end of the training loop, we should have a final reward close to 0which demonstrates that the pendulum is upward and still as desired.

batch_size = 32pbar = tqdm.tqdm(range(20_000 // batch_size))scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, 20_000)logs = defaultdict(list)for _ in pbar: init_td = env.reset(env.gen_params(batch_size=[batch_size])) rollout = env.rollout(100, policy, tensordict=init_td, auto_reset=False) traj_return = rollout["next", "reward"].mean() (-traj_return).backward() gn = torch.nn.utils.clip_grad_norm_(net.parameters(), 1.0) optim.step() optim.zero_grad() pbar.set_description( f"reward: {traj_return: 4.4f}, " f"last reward: {rollout[..., -1]['next', 'reward'].mean(): 4.4f}, gradient norm: {gn: 4.4}" ) logs["return"].append(traj_return.item()) logs["last_reward"].append(rollout[..., -1]["next", "reward"].mean().item()) scheduler.step()def plot(): import matplotlib from matplotlib import pyplot as plt is_ipython = "inline" in matplotlib.get_backend() if is_ipython: from IPython import display with plt.ion(): plt.figure(figsize=(10, 5)) plt.subplot(1, 2, 1) plt.plot(logs["return"]) plt.title("returns") plt.xlabel("iteration") plt.subplot(1, 2, 2) plt.plot(logs["last_reward"]) plt.title("last reward") plt.xlabel("iteration") if is_ipython: display.display(plt.gcf()) display.clear_output(wait=True) plt.show()plot()

Writing your environment and transforms with TorchRL — torchrl main documentation (2)

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Conclusion¶

In this tutorial, we have learned how to code a stateless environment fromscratch. We touched the subjects of:

The four essential components that need to be taken care of when codingan environment (step, reset, seeding and building specs).We saw how these methods and classes interact with theTensorDict class;
How to test that an environment is properly coded usingcheck_env_specs();
How to append transforms in the context of stateless environments and howto write custom transformations;
How to train a policy on a fully differentiable simulator.