Description
1 Introduction
This assignment requires you to implement and evaluate a pipeline for exploration and offline learning. You will first implement an exploration method called random network distillation (RND) and collect data using this exploration procedure, then perform offline training on the data collected via RND using conservative Qlearning (CQL) and finally finetune the resulting policy in the environment. You will also explore with variants of exploration bonuses – where a bonus is provided alongside the actual environment reward for exploration. This assignment would be easier to run on a CPU as we will be using gridworld domains of varying difficulties to train our agents.
1.1 File overview
The starter code for this assignment can be found at
https://github.com/berkeleydeeprlcourse/homework_fall2020/tree/master/hw5
We will be building on the code that we have implemented in the first four assignments, primarily focusing on code from Homework 3. All files needed to run your code are in the hw5 folder, but there will be some blanks you will fill with your previous solutions. Note that in your sample trajectory function, you will need to uncomment the line “ac = ac[0]”. Places that require previous code are marked with # TODO and are found in the following files:
• infrastruction/utils.py
• infrastructure/rl trainer.py
• policies/MLP policy.py
• policies/argmax policy.py
• critics/dqn critic.py
In order to implement RND and CQL, you will be writing new code in the following files:
• critics/cql critic.py
Figure 1: Figures depicting the easy (left), medium (middle) and hard (right) environments.
1.2 Environments
Unlike previous assignments, we will consider some stochastic dynamics, discrete-action gridworld environments in this assignment. The three gridworld environments you will need for the graded part of this assignment are of varying difficulty: easy, medium and hard. A picture of these environments is shown below. The easy environment requires following two hallways with a right turn in the middle. The medium environment is a maze requiring multiple turns. The hard environment is a four-rooms task which requires navigating between multiple rooms through narrow passages to reach the goal location. We also provide a very hard environment for the bonus (optional) part of this assignment.
1.3 Random Network Distillation (RND) Algorithm
A common way of doing exploration is to visit states with a large prediction error of some quantity, for instance, the TD error or even random functions. The RND algorithm, as covered in Lecture 13, aims at encouraging exploration by asking the exploration policy to more frequently undertake transitions where the prediction error of a random neural network function is high. Formally, let fθ∗(s0) be a randomly chosen vector-valued function represented by a neural network. RND trains another neural network, fˆφ(s0) to match the predictions of ) under the distribution of datapoints in the buffer, as shown below:
φ∗ = argmin. (1)
φ
If a transition (s,a,s0) is in the distribution of the data buffer, the prediction error Eφ(s0) is expected to be small. On the other hand, for all unseen state-action tuples it is expected to be large. To utilize this prediction error as a reward bonus for exploration, RND trains two critics – an exploitation critic, QR(s,a), and an exploration critic, QE(s,a), where the exploitation critic estimates the return of the policy under the actual reward function and the exploration critic estimates the return of the policy under the reward bonus. In practice, we normalize error before passing it into the exploration critic, as this value can vary widely in magnitude across states leading to poor optimization dynamics.
In this problem, we represent the random functions utilized by RND, fθ∗(s0) and fˆφ(s0) via random neural networks. To prevent the neural networks from having zero prediction error right from the beginning, we initialize the networks using two different initialization schemes marked as init_method_1 and init_method_2 in exploration/rnd_model.py.
1.4 Conservative Q-Learning
For the offline RL part of this assignment, we will implement the conservative Q-learning (CQL) algorithm that augments the Q-function training with a regularizer that minimizes the soft-maximum of the Q-values log(Pa exp(Q(s,a))) and maximizes the Q-value on the state-action pair seen in the dataset, Q(s,a). The overall CQL objective is given by the standard TD error objective augmented with the CQL regularizer weighted by . You will tweak this value of α in later questions in this assignment.
1.5 Implementation
Once you implement RND, answering some of the questions will require changing hyperparameters, which should be done by changing the command line arguments passed to run_hw5_expl.py or by modifying the parameters of the Args class from within the Colab notebook.
1.6 Evaluation
Once you have a working implementation of RND and CQL, you should prepare a report. The report should consist of one figure for each question below (each part has multiple questions). You should turn in the report as one PDF and a zip file with your code. If your code requires special instructions or dependencies to run, please include these in a file called README inside the zip file.
1.7 Problems
Part 1: “Unsupervised” RND and exploration performance. Implement the RND algorithm and use the argmax policy with respect to the exploration critic to generate state-action tuples to populate the replay buffer for the algorithm. In the code, this happens before the number of iterations crosses num_exploration_steps, which is set to 10k by default. You need to collect data using the ArgmaxPolicy policy which chooses to perform actions that maximize the exploration critic value.
In experiment log directories, you will find heatmap plots visualizing the state density in the replay buffer, as well as other helpful visuals. You will find these in the experiment log directory, as they are output during training. Pick two of the three environments and compare RND exploration to random (epsilongreedy) exploration. Include all the state density plots and a comparative evaluation of the learning curves obtained via RND and random exploration in your report.
python cs285/scripts/run_hw5_expl.py –env_name *Chosen Env 1* –use_rnd –unsupervised_exploration –exp_name q1_env1_rnd
python cs285/scripts/run_hw5_expl.py –env_name *Chosen Env 1* –unsupervised_exploration –exp_name q1_env1_random
python cs285/scripts/run_hw5_expl.py –env_name *Chosen Env 2* –use_rnd –unsupervised_exploration –exp_name q1_env2_rnd
python cs285/scripts/run_hw5_expl.py –env_name *Chosen Env 2* –unsupervised_exploration –exp_name q1_env2_random
For debugging this problem, note that on the easy environment we would expect to obtain a mean reward (100 episodes) of -25 within 4000 iterations of online exploitation. The density of the state-action pairs on this easy environment should be, as expected, more uniformly spread over the reachable parts of the environment (that are not occupied by walls) with RND as compared to random exploration where most of the density would be concentrated around the starting state.
For the second sub-part of this problem, you need to implement a separate exploration strategy of your choice. This can be an existing method, but feel free to design one of your own. To provide some starting ideas, you could try out count-based exploration methods (such as pseudo counts and EX2) or prediction error based approaches (such as exploring states with high TD error) or approaches that maximize marginal state entropy. Compare and contrast the chosen scheme with respect to RND, and specify possible reasons for the trends you see in performance. The heatmaps and trajectory visualizations will likely be helpful in understanding the behavior here.
python cs285/scripts/run_hw5_expl.py –env_name PointmassMedium-v0
–unsupervised_exploration <add arguments for your method> –exp_name q1_alg_med
python cs285/scripts/run_hw5_expl.py –env_name PointmassHard-v0
–unsupervised_exploration <add arguments for your method> –exp_name q1_alg_hard
Part 2: Offline learning on exploration data. Now that we have implemented RND for collecting exploration data that is (likely) useful for performing exploitation, we will perform offline RL on this dataset and see how close the resulting policy is to the optimal policy. To begin, you will implement the conservative Q-learning algorithm in this question which primarily needs to be added in critic/cql_critic.py and you need to use the CQL critic as the extrinsic critic in agents/explore_or_exploit_agent.py. Once CQL is implemented, you will evaluate it and compare it to a standard DQN critic.
For the first sub-part of this problem, you will write down the logic for disabling data collection in agents/explore_or_exploit_agent.py after exploitation begins and only evaluate the performance of the extrinsic critic after training on the data collected by the RND critic. To begin, run offline training at the default value of num_exploration_steps which is set to 10000. Compare DQN to CQL on the medium environment.
# cql_alpha = 0 => DQN, cql_alpha = 0.1 => CQL
python cs285/scripts/run_hw5_expl.py –env_name PointmassMedium-v0 –exp_name q2_dqn –use_rnd –unsupervised_exploration –offline_exploitation –cql_alpha=0
python cs285/scripts/run_hw5_expl.py –env_name PointmassMedium-v0 –exp_name q2_cql –use_rnd –unsupervised_exploration –offline_exploitation –cql_alpha=0.1
Examine the difference between the Q-values on state-action tuples in the dataset learned by CQL vs DQN. Does CQL give rise to Q-values that underestimate the Q-values learned via a standard DQN? If not, why? To answer this question, first you might find it illuminating to try the experiment shown below, marked as a hint and then reason about a common cause behind both of these phenomena.
Hint: Examine the performance of CQL when utilizing a transformed reward function for training the exploitation critic. Do not change any code in the environment class, instead make this change in agents/explore_or_exploit_agent.py. The transformed reward function is given by:
r˜(s,a) = (r(s,a) + shift) × scale
The choice of shift and scale is up to you, but we used shift = 1, and scale = 100. On any one domain of your choice test the performance of CQL with this transformed reward. Is it better or worse? What do you think is the reason behind this difference in performance, if any?
For the second sub-part of this problem, perform an ablation study on the performance of the offline algorithm as a function of the amount of exploration data. In particular vary the amount of exploration data for atleast two values of the variable num_exploration_steps in the offline setting and report a table of performance of DQN and CQL as a function of this amount. You need to do it on the medium or hard environment. Feel free to utilize the scaled and shifted rewards if they work better with CQL for you.
python cs285/scripts/run_hw5_expl.py –env_name *Chosen Env* –use_rnd –num_exploration_steps=[5000, 15000] –offline_exploitation –cql_alpha=0.1 –unsupervised_exploration –exp_name q2_cql_numsteps_[num_exploration_steps]
python cs285/scripts/run_hw5_expl.py –env_name *Chosen Env* –use_rnd –num_exploration_steps=[5000, 15000] –offline_exploitation –cql_alpha=0.0
–unsupervised_exploration –exp_name q2_dqn_numsteps_[num_exploration_steps]
For the third sub-part of this problem, perform a sweep over two informative values of the hyperparameter α besides the one you have already tried (denoted as cql_alpha in the code; some potential values shown in the run command below) to find the best value of α for CQL. Report the results for these values in your report and compare it to CQL with the previous α and DQN on the medium environment. Feel free to utilize the scaled and shifted rewards if they work better for CQL.
python cs285/scripts/run_hw5_expl.py –env_name PointmassMedium-v0 –use_rnd –unsupervised_exploration –offline_exploitation –cql_alpha=[0.02, 0.5]
–exp_name q2_alpha[cql_alpha]
Interpret your results for each part. Why or why not do you expect one algorithm to be better than the other? Do the results align with this expectation? If not, why?
Part 3: “Supervised” exploration with mixed reward bonuses. So far we have looked at an “unsupervised” exploration procedure – where we just train the exploration critic In this part, we will implement a different variant of RND exploration that will not utilize the exploration reward and the environment reward separately (as you did in Part 1), but will use a combination of both rewards for exploration as compared to performing fully “supervised” exploration via the RND critic and then finetune the resulting exploitation policy in the environment. To do so, you will modify the exploration_critic to utilize a weighted sum of the RND bonus and the environment reward of the form:
rmixed = explore weight × rexplore + exploit weight × renv
The weighting is controlled in agents/explore_or_exploit_agent.py. The exploitation critic is only trained on the environment reward and is used for evaluation. Once you have implemented this mechanism, run this part using:
python cs285/scripts/run_hw5_expl.py –env_name PointmassMedium-v0 –use_rnd –num_exploration_steps=20000 –cql_alpha=0.0 –exp_name q3_medium_dqn
python cs285/scripts/run_hw5_expl.py –env_name PointmassMedium-v0 –use_rnd –num_exploration_steps=20000 –cql_alpha=1.0 –exp_name q3_medium_cql
python cs285/scripts/run_hw5_expl.py –env_name PointmassHard-v0 –use_rnd –num_exploration_steps=20000 –cql_alpha=0.0 –exp_name q3_hard_dqn
python cs285/scripts/run_hw5_expl.py –env_name PointmassHard-v0 –use_rnd –num_exploration_steps=20000 –cql_alpha=1.0 –exp_name q3_hard_cql
Feel free to utilize the scaled and shifted rewards if they work better with CQL for you. For these experiments, compare the performance of this part to the second sub-part of Part 2 (i.e. results obtained via purely offline learning in Part 2) for a given number of num_exploration_steps. Include the learning curves for both DQN and CQL-based exploitation critics on these environments in your report.
Further, how do the results compare to Part 1, for the default value of num_exploration_steps? How effective is (supervised) exploration with a combination of both rewards as compared to purely RND based (unsupervised) exploration and why?
Evaluate this part on the medium and hard environments. As a debugging hint, for the hard environment, with a reward transformation of scale = 100 and shift = 1, you should find that CQL is better than DQN.
Part 4: Bonus problem (ungraded, but counts towards extra credits and we encourage you to try it). We also provide a harder layout of the gridworld environment in the environment file located at envs/pointmass/pointmass.py. Try out your exploration methods on one or more of these domains, and explore how offline training and online finetuning work in these domains. Report any interesting findings including state-visitation heatmaps, and policy performance.
2 Submitting the code and experiment runs
In order to turn in your code and experiment logs, create a folder that contains the following:
• The cs285 folder with all the .py files, with the same names and directory structure as the original homework repository (excluding the data folder). Also include any special instructions we need to run in order to produce each of your figures or tables (e.g. “run python myassignment.py -sec2q1” to generate the result for Section 2 Question 1) in the form of a README file.
As an example, the unzipped version of your submission should result in the following file structure. Make sure that the submit.zip file is below 15MB and that they include the prefix q1 , q2 , q3 , etc.
q1… events.out.tfevents.1567529456.e3a096ac8ff4 q2…
events.out.tfevents.1567529456.e3a096ac8ff4
agents explore or exploit agent.py
…
policies
…
Turn in your assignment on Gradescope. Upload the zip file with your code and log files to HW5 Code, and upload the PDF of your report to HW5.
