cs285_hw3

Part 1 of this assignment requires you to implement and evaluate Q-learning with convolutional neural networks for playing Atari games. The Q-learning algorithm was covered in lecture, and you will be provided with starter code. A GPU machine will be faster, but you should be able to get good results with about 20 hours of compute on a modern CPU.

Please start early! The questions will require you to perform multiple runs of Qlearning, each of which can take quite a long time. Furthermore, depending on your implementation, you may find it necessary to tweak some of the parameters, such as learning rates or exploration schedules, which can also be very time consuming. The actual coding for this assignment will involve about 50 lines of code, but the evaluation may take a very long time.

1.2 Installation

Obtain the code from https://github.com/berkeleydeeprlcourse/homework_ fall2019/tree/master/hw3. To run the code, go into the hw3 directory and simply execute python cs285/scripts/run_hw3_dqn.py. It will not work until you finish implementing the algorithm in agents/dqn_agent.py, critics/dqn_critic.py and policies/argmax_policy.py, in addition to copying the relevant bits that you implemented in HW1 and HW2, as specified in the README.

To install OpenCV, run pip install opencv-python==3.4.0.12. The only additional dependency you might have to install is gym[atari]==0.10.11, and you can skip this step if you installed gym[all] at the start of the class. You also need to replace <pathtogym>/gym/envs/box2d/lunar_lander.py with the provided lunar_lander.py file. To find the path, run:

locate lunar_lander.py

(or if there are multiple options run):

source activate cs285_env ipython import gym gym.__file__

which will print <pathtogym>/gym/__init__.py.

You may want to look at scripts/run_hw3_dqn.py before starting the implementation.

1.3 Implementation

The first phase of the assignment is to implement a working version of Qlearning. The default code will run the Pong game with reasonable hyperparameter settings. The starter code already provides you with a working replay buffer, and you have to fill in parts of agents/dqn_agent.py, critics/dqn_critic.py and policies/argmax_policy.py by searching for TODO. You will also need to reuse your code from homework 1 and homework 2 (e.g., utils.py, tf_utils.py, rl_trainer.py) The comments in the code describe what should be implemented in each section. You could look inside infrastructure/dqn_utils.py to understand how the replay buffer works, but you should not need to modify it. You may also look inside run_hw3_dqn.py to change the hyperparameters or the particular choice of Atari game. Once you implement Q-learning, answering some of the questions may require changing hyperparameters, neural network architectures, and the game, which should be done by changing the command line arguments passed to run_hw3_dqn.py.

To determine if your implementation of Q-learning is performing well, you should run it with the default hyperparameters on the Pong game (see the command below). Our reference solution gets a reward of around -20 to -15 after 500k steps, -15 to -10 after 1m steps, -10 to -5 after 1.5m steps, and around +10 after 2m steps on Pong. The maximum score of around +20 is reached after about 4-5m steps. However, there is considerable variation between runs. For Q1, you must run the algorithm for at least 3m timesteps (and you are encouraged to run for more), and you must achieve a final reward of at least +10 (i.e. your trained agent beats the opponent by an average of 10 points).

To accelerate debugging, you may also try out LunarLander-v2, which trains your agent to play Lunar Lander, a 1979 arcade game (also made by Atari) that has been implemented in OpenAI Gym. Note that you cannot run lunar remotely because it opens a window to render images. Our reference solution with the default hyperparameters achieves around 150 reward after 500k timesteps, but there is considerable variation between runs, and the method sometimes experience instabilities (i.e. the reward goes down after achieving 150). We recommend using LunarLander-v2 to check the correctness of your code before running longer experiments with PongNoFrameSkip-v4.

1.4 Evaluation

Once you have a working implementation of Q-learning, you should prepare a report. The report should consist of one figure for each question below. 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. For all the questions below, it is your choice how long to run for. Although running for 2-4m steps is ideal for a solid evaluation, especially when running on CPU, this may be difficult. You need to include at least one run of 3m steps for Question 1.

Question 1: basic Q-learning performance. (DQN) Include a learning curve plot showing the performance of your implementation on the game Pong. The x-axis should correspond to number of time steps (consider using scientific notation) and the y-axis should show the mean 100-episode reward as well as the best mean reward. These quantities are already computed and printed in the starter code. They are also logged to the data folder, and can be visualized using Tensorboard, similar to previous assignments. Be sure to label the y-axis, since we need to verify that your implementation achieves similar reward as ours. You should not need to modify the default hyperparameters in order to obtain good performance, but if you modify any of the parameters, list them in the caption of the figure. The final results should use the following experiment name:

python run_hw3_dqn.py env_name PongNoFrameskip-v4 exp_name

, q1

Question 2: double Q-learning (DDQN). Use the double estimator to improve the accuracy of your learned Q values. This amounts to using the online Q network (instead of the target Q network) to select the best action when computing target values. Compare the performance of DDQN to vanilla DQN. Since there is considerable variance between runs, you must run at least three random seeds for both DQN and DDQN. You must use LunarLander-v2 for this question. The final results should use the following experiment names:

python run_hw3_dqn_atari.py env_name LunarLander-v2 exp_name

, q2_dqn_1 seed 1 python run_hw3_dqn_atari.py env_name LunarLander-v2 exp_name

, q2_dqn_2 seed 2 python run_hw3_dqn_atari.py env_name LunarLander-v2 exp_name

, q2_dqn_3 seed 3

python run_hw3_dqn_atari.py env_name LunarLander-v2 exp_name

, q2_doubledqn_1 double_q seed 1 python run_hw3_dqn_atari.py env_name LunarLander-v2 exp_name

, q2_doubledqn_2 double_q seed 2 python run_hw3_dqn_atari.py env_name LunarLander-v2 exp_name

, q2_doubledqn_3 double_q seed 3

You need to submit the run_logs for each of the runs from above. In your report, you must make a single graph that averages the performance across three runs for both DQN and double DQN.

Question 3: experimenting with hyperparameters. Now lets analyze the sensitivity of Q-learning to hyperparameters. Choose one hyperparameter of your choice and run at least three other settings of this hyperparameter, in addition to the one used in Question 1, and plot all four values on the same graph. Your choice what you experiment with, but you should explain why you chose this hyperparameter in the caption. Examples include: learning rates, neural network architecture, exploration schedule or exploration rule (e.g. you may implement an alternative to -greedy), etc. Discuss the effect of this hyperparameter on performance in the caption. You should find a hyperparameter that makes a nontrivial difference on performance. Note: you might consider performing a hyperparameter sweep for getting good results in Question 1, in which case its fine to just include the results of this sweep for Question 3 as well, while plotting only the best hyperparameter setting in Question 1. The final results should use the following experiment name:

python run_hw3_dqn.py env_name PongNoFrameskip-v4 exp_name

, q3_hparam1

python run_hw3_dqn.py env_name PongNoFrameskip-v4 exp_name

, q3_hparam2

python run_hw3_dqn.py env_name PongNoFrameskip-v4 exp_name

, q3_hparam3

You can replace PongNoFrameskip-v4 with LunarLander-v2 if you choose to use that environment.

Note: for Questions 1, you must submit results on the pong environment (even if you chose to test it on the lunar lander environment). For Question 2, you must submit results on the lunar lander environment. For Question 3, you can submit on either pong or lunar lander.

2 Part 2: Actor-Critic

2.1 Introduction

Part 2 of this assignment requires you to modify policy gradients (from hw2) to an actor-critic formulation. Part 2 is relatively shorter than part 1. The actual coding for this assignment will involve less than 20 lines of code. Note however that evaluation may take longer for actor-critic than policy gradient (on half-cheetah) due to the significantly larger number of training steps for the value function.

Recall the policy gradient from hw2:

.

In this formulation, we estimate the reward to go by taking the sum of rewards to go over each trajectory to estimate the Q function, and subtracting the value function baseline to obtain the advantage

In practice, the estimated advantage value suffers from high variance. Actorcritic addresses this issue by using a critic network to estimate the sum of rewards to go. The most common type of critic network used is a value function, in which case our estimated advantage becomes

A(st,at) r(st,at) + V (st+1) V (st)

In this assignment we will use the same value function network from hw2 as the basis for our critic network. One additional consideration in actor-critic is updating the critic network itself. While we can use Monte Carlo rollouts to estimate the sum of rewards to go for updating the value function network, in practice we fit our value function to the following target values:

y_t = r(s_t,a_t) + V (s_t+1)

we then regress onto these target values via the following regression objective which we can optimize with gradient descent:

minX(V(sit) yit)2

i,t

In theory, we need to perform this minimization everytime we update our policy, so that our value function matches the behavior of the new policy. In practice however, this operation can be costly, so we may instead just take a few gradient steps at each iteration. Also note that since our target values are based on the old value function, we may need to recompute the targets with the updated value function, in the following fashion:

1. Update targets with current value function
2. Regress onto targets to update value function by taking a few gradient steps
3. Redo steps 1 and 2 several times

In all, the process of fitting the value function critic is an iterative process in which we go back and forth between computing target values and updating the value function to match the target values. Through experimentation, you will see that this iterative process is crucial for training the critic network.

2.2 Implementation

Your code will build off your solutions from hw2. You will need to fill in the TODOS for the following parts of the code. To run the code, go into the hw3 directory and simply execute python cs285/scripts/run_hw3_actor_critic.py.

• In policies/MLP_policy.py copy over your policy class for PG, you should note that the AC policy class is in fact the same as the policy class you implemented in the policy gradient homework (except we no longer have a nn baseline).
• In agents/ac_agent.py, finish the train This function should implement the necessary critic updates, estimate the advantage, and then update the policy. Log the final losses at the end so you can monitor it during training.
• In agents/ac_agent.py, finish the estimate_advantage function: this function uses the critic network to estimate the advantage values. The advantage values are computed according to

Note: for terminal timesteps, you must make sure to cut off the reward to go (i.e., set it to zero), in which case we have

A(s_t,a_t) r(s_t,a_t) V (s_t)

• critics/bootstrapped_continuous_critic.py complete the TODOS in build, update and forward. In update, perform the critic update according to process outlined in the introduction. You must perform num_grad_steps_per_target_update * self.num_target_updates number of updates, and recompute the target values every self.num_grad_steps_per_target_update number of steps.

2.3 Evaluation

Once you have a working implementation of actor-critic, you should prepare a report. The report should consist of figures for the question below. You should turn in the report as one PDF (same PDF as part 1) and a zip file with your code (same zip file as part 1). If your code requires special instructions or dependencies to run, please include these in a file called README inside the zip file.

Question 4: Sanity check with Cartpole Now that you have implemented actor-critic, check that your solution works by running Cartpole-v0.

python run_hw3_actor_critic.py env_name CartPole-v0 -n 100 -b

, 1000 exp_name 1_1 -ntu 1 -ngsptu 1

In the example above, we alternate between performing one target update and one gradient update step for the critic. As you will see, this probably doesnt work, and you need to increase both the number of target updates and number of gradient updates. Compare the results for the following settings and report which worked best. Do this by plotting all the runs on a single plot and writing your takeaway in the caption.

python run_hw3_actor_critic.py env_name CartPole-v0 -n 100, 1000 exp_name 100_1 -ntu 100 -ngsptu 1	-b
python run_hw3_actor_critic.py env_name CartPole-v0 -n 100, 1000 exp_name 1_100 -ntu 1 -ngsptu 100	-b
python run_hw3_actor_critic.py env_name CartPole-v0 -n 100, 1000 exp_name 10_10 -ntu 10 -ngsptu 10	-b

At the end, the best setting from above should match the policy gradient results from Cartpole in hw2 (200).

Question 5: Run actor-critic with more difficult tasks Use the best setting from the previous question to run InvertedPendulum and HalfCheetah:

python run_hw3_actor_critic.py env_name InvertedPendulum-v2

, ep_len 1000 discount 0.95 -n 100 -l 2 -s 64 -b 5000 -lr

, 0.01 exp_name <>_<> -ntu <> -ngsptu <>

python run_hw3_actor_critic.py env_name HalfCheetah-v2

, ep_len 150 discount 0.90 scalar_log_freq 1 -n 150 -l 2

, -s 32 -b 30000 -eb 1500 -lr 0.02 exp_name <>_<> -ntu <>

, -ngsptu <>

Your results should roughly match those of policy gradient. After 150 iterations, your HalfCheetah return should be around 150 and your InveredPendulum return should be around 1000. Your deliverables for this section are plots with the eval returns for both enviornments.