Basic concepts in Tianshou¶
Tianshou splits a Reinforcement Learning agent training procedure into these parts: trainer, collector, policy, and data buffer. The general control flow can be described as:
Here is a more detailed description, where Env
is the environment and Model
is the neural network:
Batch¶
Tianshou provides Batch
as the internal data structure to pass any kind of data to other methods, for example, a collector gives a Batch
to policy for learning. Let’s take a look at this script:
>>> import torch, numpy as np
>>> from tianshou.data import Batch
>>> data = Batch(a=4, b=[5, 5], c='2312312', d=('a', -2, -3))
>>> # the list will automatically be converted to numpy array
>>> data.b
array([5, 5])
>>> data.b = np.array([3, 4, 5])
>>> print(data)
Batch(
a: 4,
b: array([3, 4, 5]),
c: '2312312',
d: array(['a', '-2', '-3'], dtype=object),
)
>>> data = Batch(obs={'index': np.zeros((2, 3))}, act=torch.zeros((2, 2)))
>>> data[:, 1] += 6
>>> print(data[-1])
Batch(
obs: Batch(
index: array([0., 6., 0.]),
),
act: tensor([0., 6.]),
)
In short, you can define a Batch
with any key-value pair, and perform some common operations over it.
Understand Batch is a dedicated tutorial for Batch
. We strongly recommend every user to read it so as to correctly understand and use Batch
.
Buffer¶
ReplayBuffer
stores data generated from
interaction between the policy and environment. The current implementation
of Tianshou typically use 7 reserved keys in Batch
:
obs
the observation of step \(t\) ;act
the action of step \(t\) ;rew
the reward of step \(t\) ;done
the done flag of step \(t\) ;obs_next
the observation of step \(t+1\) ;info
the info of step \(t\) (ingym.Env
, theenv.step()
function returns 4 arguments, and the last one isinfo
);policy
the data computed by policy in step \(t\);
The following code snippet illustrates its usage:
>>> import pickle, numpy as np
>>> from tianshou.data import ReplayBuffer
>>> buf = ReplayBuffer(size=20)
>>> for i in range(3):
... buf.add(obs=i, act=i, rew=i, done=i, obs_next=i + 1, info={})
>>> buf.obs
# since we set size = 20, len(buf.obs) == 20.
array([0., 1., 2., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0.])
>>> # but there are only three valid items, so len(buf) == 3.
>>> len(buf)
3
>>> pickle.dump(buf, open('buf.pkl', 'wb')) # save to file "buf.pkl"
>>> buf2 = ReplayBuffer(size=10)
>>> for i in range(15):
... buf2.add(obs=i, act=i, rew=i, done=i, obs_next=i + 1, info={})
>>> len(buf2)
10
>>> buf2.obs
# since its size = 10, it only stores the last 10 steps' result.
array([10., 11., 12., 13., 14., 5., 6., 7., 8., 9.])
>>> # move buf2's result into buf (meanwhile keep it chronologically)
>>> buf.update(buf2)
array([ 0., 1., 2., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14.,
0., 0., 0., 0., 0., 0., 0.])
>>> # get a random sample from buffer
>>> # the batch_data is equal to buf[incide].
>>> batch_data, indice = buf.sample(batch_size=4)
>>> batch_data.obs == buf[indice].obs
array([ True, True, True, True])
>>> len(buf)
13
>>> buf = pickle.load(open('buf.pkl', 'rb')) # load from "buf.pkl"
>>> len(buf)
3
ReplayBuffer
also supports frame_stack sampling
(typically for RNN usage, see issue#19), ignoring storing the next
observation (save memory in atari tasks), and multi-modal observation (see
issue#38):
>>> buf = ReplayBuffer(size=9, stack_num=4, ignore_obs_next=True)
>>> for i in range(16):
... done = i % 5 == 0
... buf.add(obs={'id': i}, act=i, rew=i, done=done,
... obs_next={'id': i + 1})
>>> print(buf) # you can see obs_next is not saved in buf
ReplayBuffer(
act: array([ 9., 10., 11., 12., 13., 14., 15., 7., 8.]),
done: array([0., 1., 0., 0., 0., 0., 1., 0., 0.]),
info: Batch(),
obs: Batch(
id: array([ 9., 10., 11., 12., 13., 14., 15., 7., 8.]),
),
policy: Batch(),
rew: array([ 9., 10., 11., 12., 13., 14., 15., 7., 8.]),
)
>>> index = np.arange(len(buf))
>>> print(buf.get(index, 'obs').id)
[[ 7. 7. 8. 9.]
[ 7. 8. 9. 10.]
[11. 11. 11. 11.]
[11. 11. 11. 12.]
[11. 11. 12. 13.]
[11. 12. 13. 14.]
[12. 13. 14. 15.]
[ 7. 7. 7. 7.]
[ 7. 7. 7. 8.]]
>>> # here is another way to get the stacked data
>>> # (stack only for obs and obs_next)
>>> abs(buf.get(index, 'obs')['id'] - buf[index].obs.id).sum().sum()
0.0
>>> # we can get obs_next through __getitem__, even if it doesn't exist
>>> print(buf[:].obs_next.id)
[[ 7. 8. 9. 10.]
[ 7. 8. 9. 10.]
[11. 11. 11. 12.]
[11. 11. 12. 13.]
[11. 12. 13. 14.]
[12. 13. 14. 15.]
[12. 13. 14. 15.]
[ 7. 7. 7. 8.]
[ 7. 7. 8. 9.]]
- param int size
the size of replay buffer.
- param int stack_num
the frame-stack sampling argument, should be greater than or equal to 1, defaults to 1 (no stacking).
- param bool ignore_obs_next
whether to store obs_next, defaults to
False
.- param bool sample_avail
the parameter indicating sampling only available index when using frame-stack sampling method, defaults to
False
. This feature is not supported in Prioritized Replay Buffer currently.
-
tianshou.data.ReplayBuffer.
__setattr__
(self, name, value, /) Implement setattr(self, name, value).
Tianshou provides other type of data buffer such as ListReplayBuffer
(based on list), PrioritizedReplayBuffer
(based on Segment Tree and numpy.ndarray
). Check out ReplayBuffer
for more detail.
Policy¶
Tianshou aims to modularizing RL algorithms. It comes into several classes of policies in Tianshou. All of the policy classes must inherit BasePolicy
.
A policy class typically has the following parts:
__init__()
: initialize the policy, including copying the target network and so on;forward()
: compute action with given observation;process_fn()
: pre-process data from the replay buffer;learn()
: update policy with a given batch of data.post_process_fn()
: update the buffer with a given batch of data.update()
: the main interface for training. This function samples data from buffer, pre-process data (such as computing n-step return), learn with the data, and finally post-process the data (such as updating prioritized replay buffer); in short,process_fn -> learn -> post_process_fn
.
Take 2-step return DQN as an example. The 2-step return DQN compute each frame’s return as:
where \(\gamma\) is the discount factor, \(\gamma \in [0, 1]\). Here is the pseudocode showing the training process without Tianshou framework:
# pseudocode, cannot work
s = env.reset()
buffer = Buffer(size=10000)
agent = DQN()
for i in range(int(1e6)):
a = agent.compute_action(s)
s_, r, d, _ = env.step(a)
buffer.store(s, a, s_, r, d)
s = s_
if i % 1000 == 0:
b_s, b_a, b_s_, b_r, b_d = buffer.get(size=64)
# compute 2-step returns. How?
b_ret = compute_2_step_return(buffer, b_r, b_d, ...)
# update DQN policy
agent.update(b_s, b_a, b_s_, b_r, b_d, b_ret)
Thus, we need a time-related interface for calculating the 2-step return. process_fn()
finishes this work by providing the replay buffer, the sample index, and the sample batch data. Since we store all the data in the order of time, you can simply compute the 2-step return as:
class DQN_2step(BasePolicy):
"""some code"""
def process_fn(self, batch, buffer, indice):
buffer_len = len(buffer)
batch_2 = buffer[(indice + 2) % buffer_len]
# this will return a batch data where batch_2.obs is s_t+2
# we can also get s_t+2 through:
# batch_2_obs = buffer.obs[(indice + 2) % buffer_len]
# in short, buffer.obs[i] is equal to buffer[i].obs, but the former is more effecient.
Q = self(batch_2, eps=0) # shape: [batchsize, action_shape]
maxQ = Q.max(dim=-1)
batch.returns = batch.rew \
+ self._gamma * buffer.rew[(indice + 1) % buffer_len] \
+ self._gamma ** 2 * maxQ
return batch
This code does not consider the done flag, so it may not work very well. It shows two ways to get \(s_{t + 2}\) from the replay buffer easily in process_fn()
.
For other method, you can check out tianshou.policy. We give the usage of policy class a high-level explanation in A High-level Explanation.
Collector¶
The Collector
enables the policy to interact with different types of environments conveniently.
Collector
has one main method collect()
: it let the policy perform (at least) a specified number of step n_step
or episode n_episode
and store the data in the replay buffer.
Why do we mention at least here? For multiple environments, we could not directly store the collected data into the replay buffer, since it breaks the principle of storing data chronologically.
The solution is to add some cache buffers inside the collector. Once collecting a full episode of trajectory, it will move the stored data from the cache buffer to the main buffer. To satisfy this condition, the collector will interact with environments that may exceed the given step number or episode number.
The general explanation is listed in A High-level Explanation. Other usages of collector are listed in Collector
documentation.
Trainer¶
Once you have a collector and a policy, you can start writing the training method for your RL agent. Trainer, to be honest, is a simple wrapper. It helps you save energy for writing the training loop. You can also construct your own trainer: Train a Policy with Customized Codes.
Tianshou has two types of trainer: onpolicy_trainer()
and offpolicy_trainer()
, corresponding to on-policy algorithms (such as Policy Gradient) and off-policy algorithms (such as DQN). Please check out tianshou.trainer for the usage.
A High-level Explanation¶
We give a high-level explanation through the pseudocode used in section Policy:
# pseudocode, cannot work # methods in tianshou
s = env.reset()
buffer = Buffer(size=10000) # buffer = tianshou.data.ReplayBuffer(size=10000)
agent = DQN() # policy.__init__(...)
for i in range(int(1e6)): # done in trainer
a = agent.compute_action(s) # policy(batch, ...)
s_, r, d, _ = env.step(a) # collector.collect(...)
buffer.store(s, a, s_, r, d) # collector.collect(...)
s = s_ # collector.collect(...)
if i % 1000 == 0: # done in trainer
# the following is done in policy.update(batch_size, buffer)
b_s, b_a, b_s_, b_r, b_d = buffer.get(size=64) # buffer.sample(batch_size)
# compute 2-step returns. How?
b_ret = compute_2_step_return(buffer, b_r, b_d, ...) # policy.process_fn(batch, buffer, indice)
# update DQN policy
agent.update(b_s, b_a, b_s_, b_r, b_d, b_ret) # policy.learn(batch, ...)
Conclusion¶
So far, we go through the overall framework of Tianshou. Really simple, isn’t it?