Description
1 Introduction
The goal of this assignment is to get experience with model-based reinforcement
learning. In general, model-based reinforcement learning consists of two main
parts: learning a dynamics function to model observed state transitions, and
then using predictions from that model in some way to decide what to do (e.g.,
use model predictions to learn a policy, or use model predictions directly in an
optimization setup to maximize predicted rewards).
In this assignment, you will do the latter. You will implement both the process
of learning a dynamics model, as well as the process of creating a controller
to perform action selection through the use of these model predictions. For
references to this type of approach, see this paper and this paper.
2 Model-Based Reinforcement Learning
We will now provide a brief overview of model-based reinforcement learning
(MBRL), and the specific type of MBRL you will be implementing in this
homework. Please see Lecture 11: Model-Based Reinforcement Learning (with
specific emphasis on the slides near page 10) for additional details.
MBRL consists primarily of two aspects: (1) learning a dynamics model and (2)
using the learned dynamics models to plan and execute actions that minimize
a cost function (or maximize a reward function).
2.1 Dynamics Model
In this assignment, you will learn a neural network dynamics model of the
form:
∆ˆ
t+1 = fθ(st, at) (1)
1
such that
ˆst+1 = st + ∆ˆ
t+1 (2)
in which the neural network fθ encodes the change in state that occurs as a result
of executing the action at from state st. See the previously referenced paper
for intuition on why we might want our network to predict state differences,
instead of directly predicting next state.
You will train fθ in a standard supervised learning setup, by performing gradient
descent on the following objective:
L(θ) = X
(st,at,st+1)∈D
k(st+1 − st) − fθ(st, at)k
2
2
(3)
=
X
(st,at,st+1)∈D
k∆t+1 − ∆ˆ
t+1k
2
2
(4)
2.2 Action Selection
Given the learned dynamics model, we now want to select and execute actions
that minimize a known cost function (or maximize a known reward function).
Ideally, you would calculate these actions by solving the following optimization:
a
∗
t = arg min
at:∞
X∞
t
0=t
c(ˆst
0 , at
0 ) s.t. ˆst
0+1 = ˆst
0 + fθ(ˆst
0 , at
0 ). (5)
However, solving Eqn. 5 is impractical for two reasons: (1) planning over an
infinite sequence of actions is impossible and (2) the learned dynamics model
is imperfect, so using it to plan in such an open-loop manner will lead to accumulating errors over time and planning far into the future will become very
inaccurate.
Instead, we will solve the following gradient-free optimization problem:
A∗ = arg min
{A(0),…,A(K−1)}
t+
X
H−1
t
0=t
c(ˆst
0 , at
0 ) s.t. ˆst
0+1 = ˆst
0 + fθ(ˆst
0 , at
0 ), (6)
in which A(k) are each a random action sequence of length H. What Eqn. 6
says is to consider K random action sequences of length H, predict the result
(i.e., future states) of taking each of these action sequences using the learned
dynamics model fθ, evaluate the cost/reward associated with each candidate
action sequence, and select the best action sequence. Note that this approach
only plans H steps into the future, which is desirable because it prevent accumulating model error, but is also not desirable because it may not be sufficient
for solving long-horizon tasks.
2
Additionally, since our model is imperfect and things will never go perfectly according to plan, we adopt a model predictive control (MPC) approach, in which
we solve Eqn. 6 at every time step to select the best H-step action sequence,
but then we execute only the first action from that sequence before replanning
again at the next time step using updated state information.
Finally, note that the random-shooting optimization approach mentioned above
can be greatly improved (see this paper).
2.3 On-Policy Data Collection
Although MBRL is in theory off-policy—meaning it can learn from any data—in
practice it will perform poorly if you don’t have on-policy data. In other words,
if a model is trained on only randomly-collected data, it will (in most cases) be
insufficient to describe the parts of the state space that we may actually care
about. We can therefore use on-policy data collection in an iterative algorithm
to improve overall task performance. This is summarized as follows:
Algorithm 1 Model-Based Reinforcement Learning with On-Policy Data
Run base policy π0(at, st) (e.g., random policy) to collect D = {(st, at, st+1)}
while not done do
Train fθ using D (Eqn. 4)
st ← current agent state
for rollout number m = 0 to M do
for timestep t = 0 to T do
A∗ = πMPC(at, st) where πMPC is the process of optimizing Eqn. 6
at ← first action in A∗
Execute at and proceed to next state st+1
Add (st, at, st+1) to D
end
end
end
3 Code
You will implement the MBRL algorithm described in the previous section.
3.1 Overview
Obtain the code from https://github.com/berkeleydeeprlcourse/
homework_fall2019/tree/master/hw4.
You will add code to the following three files: agents/mb agent.py, models/ff model.py, and policies/MPC policy.py. You will also need to edit these files
3
by copying your code from past homeworks: infrastructure/rl_trainer.py,
infrastructure/utils.py, infrastructure/tf_utils.py
4
Problem 1
What you will implement:
Collect a large dataset by executing random actions. Train a neural network
dynamics model on this fixed dataset and visualize the resulting predictions.
The implementation that you will do here will be for training the dynamics
model, and comparing its predictions against ground truth. You will be reusing
the utilities you wrote for HW1 for the data collection part (look for ”GET
THIS FROM HW1” markers).
What code files to fill in:
1. cs285/agents/mb_agent.py
2. cs285/models/ff_model.py
3. cs285/infrastructure/utils.py
4. cs285/policies/MPC_policy.py (just one line)
What commands to run:
python cs285/scripts/run_hw4_mb.py –exp_name cheetah_n500_arch1x32 —
env_name cheetah-cs285-v0 –add_sl_noise –n_iter 1 —
batch_size_initial 20000 –num_agent_train_steps_per_iter 500 —
n_layers 1 –size 32 –scalar_log_freq -1 –video_log_freq -1
python cs285/scripts/run_hw4_mb.py –exp_name cheetah_n5_arch2x250 —
env_name cheetah-cs285-v0 –add_sl_noise –n_iter 1 —
batch_size_initial 20000 –num_agent_train_steps_per_iter 5 —
n_layers 2 –size 250 –scalar_log_freq -1 –video_log_freq -1
python cs285/scripts/run_hw4_mb.py –exp_name cheetah_n500_arch2x250 —
env_name cheetah-cs285-v0 –add_sl_noise –n_iter 1 —
batch_size_initial 20000 –num_agent_train_steps_per_iter 500 —
n_layers 2 –size 250 –scalar_log_freq -1 –video_log_freq -1
Your code will produce plots inside your logdir that illustrate your model prediction error (MPE). These plots illustrate, for a fixed action sequence, the
difference between your model’s predictions (red) and the ground-truth states
(green). Each plot corresponds to a different state element, and the title reports the mean mean-squared-error across all state elements. As illustrated in
the commands above, try different neural network architectures as well different
amounts of training. Compare the results by looking at the loss values (i.e.,
itr 0 losses.png), the qualitative model predictions (i.e., itr 0 predictions.png),
as well as the quantitative MPE values (i.e., in the title of itr 0 predictions.png).
What to submit: For this question, submit
(a) these runs as part of your run logs,
(b) qualitative model prediction plots for the 2 network sizes (runs 1,2)
(c) qualitative model prediction plots for the 2 of training steps (runs 2,3)
Note that for these qualitative model prediction plots, we intend for you to just
copy the png images produced by the code.
5
Problem 2
What will you implement:
Action selection using your learned dynamics model and a given reward function.
What code files to fill in:
1. cs285/policies/MPC_policy.py
What commands to run:
python cs285/scripts/run_hw4_mb.py –exp_name obstacles_singleiteration
–env_name obstacles-cs285-v0 –add_sl_noise —
num_agent_train_steps_per_iter 20 –n_iter 1 –batch_size_initial
5000 –batch_size 1000 –mpc_horizon 10
Recall the overall flow of our rl trainer.py. We first collect data with our policy
(which starts as random), we then train our model on that collected data, and
we then evaluate the resulting MPC policy (which now uses the trained model).
To verify that your MPC is indeed doing reasonable action selection, run the
command above and compare Train AverageReturn (which was the execution
of random actions) to Eval AverageReturn (which was the execution of MPC
using a model that was trained on the randomly collected training data). You
can expect Train AverageReturn to be around -160 and Eval AverageReturn to
be around -70 to -50.
What to submit:
Submit this run as part of your run logs, and include a plot of Train AverageReturn
and Eval AverageReturn in your pdf. Note that these will just be single dots
on the plot, since we ran this for just 1 iteration.
6
Problem 3
What will you implement:
MBRL algorithm with on-policy data collection and iterative model training.
What code files to fill in:
None. You should already have done everything that you need, because rl trainer.py
already aggregates your collected data into a replay buffer. Thus, iterative training means to just train on our growing replay buffer while collecting new data
at each iteration using the most newly trained model.
What commands to run:
python cs285/scripts/run_hw4_mb.py –exp_name obstacles –env_name
obstacles-cs285-v0 –add_sl_noise –num_agent_train_steps_per_iter
20 –batch_size_initial 5000 –batch_size 1000 –mpc_horizon 10 —
n_iter 12
python cs285/scripts/run_hw4_mb.py –exp_name reacher –env_name
reacher-cs285-v0 –add_sl_noise –mpc_horizon 10 —
num_agent_train_steps_per_iter 1000 –batch_size_initial 5000 —
batch_size 5000 –n_iter 15
python cs285/scripts/run_hw4_mb.py –exp_name cheetah –env_name
cheetah-cs285-v0 –mpc_horizon 15 –add_sl_noise —
num_agent_train_steps_per_iter 1500 –batch_size_initial 5000 —
batch_size 5000 –n_iter 20
You should expect rewards of around -25 to -20 for the obstacles env (takes 40
minutes), rewards of around -250 to -300 for the reacher env (takes 2-3 hours),
and rewards of around 250-350 for the cheetah env takes 3-4 hours. All numbers
assume no GPU.
What to submit:
Submit these runs as part of your run logs, and include the performance plots
in your pdf.
7
Problem 4
What will you implement:
You will compare the performance of your MBRL algorithm as a function of
three hyperparameters: the number of models in your ensemble, the number of
random action sequences considered during each action selection, and the MPC
planning horizon.
What code files to fill in:
None.
What commands to run:
python cs285/scripts/run_hw4_mb.py –exp_name q5_reacher_horizon5 —
env_name reacher-cs285-v0 –add_sl_noise –mpc_horizon 5 —
num_agent_train_steps_per_iter 1000 –batch_size 800 –n_iter 15
python cs285/scripts/run_hw4_mb.py –exp_name q5_reacher_horizon15 —
env_name reacher-cs285-v0 –add_sl_noise –mpc_horizon 15 —
num_agent_train_steps_per_iter 1000 –batch_size 800 –n_iter 15
python cs285/scripts/run_hw4_mb.py –exp_name q5_reacher_horizon30 —
env_name reacher-cs285-v0 –add_sl_noise –mpc_horizon 30 —
num_agent_train_steps_per_iter 1000 –batch_size 800 –n_iter 15
python cs285/scripts/run_hw4_mb.py –exp_name q5_reacher_numseq100 —
env_name reacher-cs285-v0 –add_sl_noise –mpc_horizon 10 —
num_agent_train_steps_per_iter 1000 –batch_size 800 –n_iter 15 —
mpc_num_action_sequences 100
python cs285/scripts/run_hw4_mb.py –exp_name q5_reacher_numseq1000 —
env_name reacher-cs285-v0 –add_sl_noise –mpc_horizon 10 —
num_agent_train_steps_per_iter 1000 –batch_size 800 –n_iter 15 —
mpc_num_action_sequences 1000
python cs285/scripts/run_hw4_mb.py –exp_name q5_reacher_ensemble1 —
env_name reacher-cs285-v0 –ensemble_size 1 –add_sl_noise —
mpc_horizon 10 –num_agent_train_steps_per_iter 1000 –batch_size
800 –n_iter 15
python cs285/scripts/run_hw4_mb.py –exp_name q5_reacher_ensemble3 —
env_name reacher-cs285-v0 –ensemble_size 3 –add_sl_noise —
mpc_horizon 10 –num_agent_train_steps_per_iter 1000 –batch_size
800 –n_iter 15
python cs285/scripts/run_hw4_mb.py –exp_name q5_reacher_ensemble5 —
env_name reacher-cs285-v0 –ensemble_size 5 –add_sl_noise —
mpc_horizon 10 –num_agent_train_steps_per_iter 1000 –batch_size
800 –n_iter 15
What to submit:
1) Submit these runs as part of your run logs.
2) Include the following plots (as well as captions that describe your observed
trends) of the following:
• effect of ensemble size
8
• effect of the number of candidate action sequences
• efffect of planning horizon
Be sure to include titles and legends on all of your plots, and be sure to generate your plots by extracting the corresponding performance numbers from your
saved tensorboard eventfiles.
9
Submission
3.2 Submitting the PDF
Your report should be a PDF document containing the plots and responses
indicated in the questions above.
3.3 Submitting the Code and Logs
In order to turn in your code and experiment logs, create a folder that contains
the following:
• A folder named run logs with all the experiment runs from this assignment. These folders can be copied directly from the cs285/data
folder. Do not change the names originally assigned to the folders, as
specified by exp name in the instructions. In order to minimize submissions size, please include runs with video logging disabled. If you would
like to reuse your video loggins rungs, please see the script provided in
cs285/scripts/filter_videos.py.
• The cs285 folder with all the .py files, with the same names and directory structure as the original homework repository (excluding the cs285/data
folder). A plotting script should also be submitted, which should be a
python script (or jupyter notebook) such that running it can generate all
plots from your pdf. This plotting script should extract its values directly
from the experiments in your run logs and should not have hardcoded
reward values.
10