Deep Reinforcement Learning – Winter 2018/19
In recent years, reinforcement learning has been combined with deep neural networks, giving rise to game agents with superhuman performance (for example for Go, chess, or 1v1 Dota2, capable of being trained solely by selfplay), datacenter cooling algorithms being 50% more efficient than trained human operators, or improved machine translation. The goal of the course is to introduce reinforcement learning employing deep neural networks, focusing both on the theory and on practical implementations.
Python programming skills and TensorFlow skills (or any other deep learning framework) are required, to the extent of the NPFL114 course. No previous knowledge of reinforcement learning is necessary.
About
SIS code: NPFL122
Semester: winter
Ecredits: 6
Examination: 2/2 C+Ex
Guarantor: Milan Straka
Timespace Coordinates
 lecture: the lecture is held on Monday 12:20 in S3; first lecture is on Oct 08
 practicals: there are two parallel practicals, on Tuesday 9:00 in SU1 and on Tuesday 10:40 in SU1; first practicals are on Oct 09
Lectures
1. Introduction to Reinforcement Learning Slides PDF Slides multiarmed_bandits
2. Markov Decision Process, Optimal Solutions, Monte Carlo Methods Slides PDF Slides policy_iteration monte_carlo
3. Temporal Difference Methods, OffPolicy Methods Slides PDF Slides q_learning importance_sampling lunar_lander
4. Nstep Methods, Function Approximation Slides PDF Slides q_learning_tiles
5. Function Approximation, Deep Q Network Slides PDF Slides q_network
6. Rainbow Slides PDF Slides car_racing reinforce
7. Policy Gradient Methods Slides PDF Slides reinforce_with_baseline cart_pole_pixels
8. Advantage ActorCritic, Continuous Action Space Slides PDF Slides paac paac_continuous
9. Deterministic Policy Gradient, Advanced RL Algorithms Slides PDF Slides ddpg walker
10. TD3, Monte Carlo Tree Search Slides PDF Slides walker_hardcore az_quiz az_quiz_randomized
11. Vtrace, PopArt Normalization, Partially Observable MDPs Slides PDF Slides vtrace memory_game
Requirements
To pass the practicals, you need to complete at least 80% of compulsory home assignments. Several additional voluntary assignments will be available, and up to 40 points from these will be transfered to the exam.
To pass the exam, you need to obtain at least 60, 75 and 90 out of 100 points for the written exam (plus up to 40 points from the practicals), to obtain grades 3, 2 and 1, respectively.
License
Unless otherwise stated, teaching materials for this course are available under CC BYSA 4.0.
The lecture content, including references to study materials.
The main study material is the Reinforcement Learning: An Introduction; second edition by Richard S. Sutton and Andrew G. Barto (reffered to as RLB). It is available online and also as a hardcopy since October 15, 2018.
References to study materials cover all theory required at the exam, and sometimes even more – the references in italics cover topics not required for the exam.
1. Introduction to Reinforcement Learning
Oct 08 Slides PDF Slides multiarmed_bandits
 History of RL [Chapter 1 of RLB]
 Multiarmed bandits [Chapter 2 of RLB]
2. Markov Decision Process, Optimal Solutions, Monte Carlo Methods
Oct 15 Slides PDF Slides policy_iteration monte_carlo
 Markov Decision Process [Sections 33.3 of RLB]
 Policies and Value Functions [Sections 3.53.6 of RLB]
 Value Iteration [Sections 4 and 4.4 of RLB]
 Proof of convergence only in slides
 Policy Iteration [Sections 4.14.3 of RLB]
 Generalized Policy Iteration [Section 4.6 or RLB]
 Monte Carlo Methods [Sections 55.4 of RLB]
3. Temporal Difference Methods, OffPolicy Methods
Oct 22 Slides PDF Slides q_learning importance_sampling lunar_lander
 Modelfree and modelbased methods, using statevalue or actionvalue functions [Chapter 8 before Section 8.1, and Section 6.8 of RLB]
 Temporaldifference methods [Sections 66.3 of RLB]
 Sarsa [Section 6.4 of RLB]
 Qlearning [Section 6.5 of RLB]
 Offpolicy Monte Carlo Methods [Sections 5.55.7 of RLB]
 Expected Sarsa [Section 6.6 of RLB]
4. Nstep Methods, Function Approximation
Nov 05 Slides PDF Slides q_learning_tiles
 Double Qlearning [Section 6.7 of RLB]
 Nstep TD policy evaluation [Section 7.1 of RLB]
 Offpolicy nstep Sarsa [Section 7.3 of RLB]
 Tree backup algorithm [Section 7.5 of RLB]
 Function approximation [Sections 99.3 of RLB]
 Tile coding [Section 9.5.4 of RLB]
5. Function Approximation, Deep Q Network
Nov 12 Slides PDF Slides q_network
 Linear function approximation [Section 9.4 of RLB, without the Proof of Convergence if Linear TD(0)]
 SemiGradient TD methods [Sections 9.3, 1010.2 of RLB]
 Offpolicy function approximation TD divergence [Sections 11.211.3 of RLB]
 Deep Q Network [Volodymyr Mnih et al.: Humanlevel control through deep reinforcement learning]
6. Rainbow
Nov 19 Slides PDF Slides car_racing reinforce
 Double Deep Q Network (DDQN) [Hado van Hasselt et al.: Deep Reinforcement Learning with Double Qlearning]
 Prioritized Experience Replay [Tom Schaul et al.: Prioritized Experience Replay]
 Dueling Deep Q Network [Ziyu Wang et al.: Dueling Network Architectures for Deep Reinforcement Learning]
 Noisy Nets [Meire Fortunato et al.: Noisy Networks for Exploration]
 Distributional Reinforcement Learning [Marc G. Bellemare et al.: A Distributional Perspective on Reinforcement Learning]
 Rainbow [Matteo Hessel et al.: Rainbow: Combining Improvements in Deep Reinforcement Learning]
7. Policy Gradient Methods
Nov 26 Slides PDF Slides reinforce_with_baseline cart_pole_pixels
 Policy Gradient Methods [Sections 1313.1 of RLB]
 Policy Gradient Theorem [Section 13.2 of RLB]
 REINFORCE algorithm [Section 13.3 of RLB]
 REINFORCE with baseline algorithm [Section 13.4 of RLB]
 ActorCritic methods [Section 13.5 of RLB, without the eligibility traces variant]
8. Advantage ActorCritic, Continuous Action Space
Dec 03 Slides PDF Slides paac paac_continuous
 A3C and asynchronous RL [Volodymyr Mnih et al.: Asynchronous Methods for Deep Reinforcement Learning]
 PAAC [Alfredo V. Clemente et al.: Efficient Parallel Methods for Deep Reinforcement Learning]
 Gradient methods with continuous actions [Section 13.7 of RLB]
9. Deterministic Policy Gradient, Advanced RL Algorithms
Dec 10 Slides PDF Slides ddpg walker
 Deterministic policy gradient theorem (DPG) [David Silver et al.: Deterministic Policy Gradient Algorithms]
 Deep deterministic policy gradient (DDPG) [Timothy P. Lillicrap et al.: Continuous Control with Deep Reinforcement Learning]
 Natural policy gradient (NPG) [Sham Kakade: A Natural Policy Gradient]
 Truncated natural policy gradient (TNPG), Trust Region Policy Optimalization (TRPO) [John Schulman et al.: Trust Region Policy Optimization]
 Proximal policy optimization (PPO) [John Schulman et al.: Proximal Policy Optimization Algorithms]
 Soft actorcritic (SAC) [Tuomas Haarnoja et al.: Soft ActorCritic: OffPolicy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor]
10. TD3, Monte Carlo Tree Search
Dec 17 Slides PDF Slides walker_hardcore az_quiz az_quiz_randomized
 Twin delayed deep deterministic policy gradient (TD3) [Scott Fujimoto et al.: Addressing Function Approximation Error in ActorCritic Methods]
 AlphaZero [David Silver et al.: A general reinforcement learning algorithm that masters chess, shogi, and Go through selfplay]
11. Vtrace, PopArt Normalization, Partially Observable MDPs
Jan 07 Slides PDF Slides vtrace memory_game
 The Vtrace algorithm of IMPALA [Lasse Espeholt et al.: IMPALA: Scalable Distributed DeepRL with Importance Weighted ActorLearner Architectures]
 PopArt reward normalization [Matteo Hessel et al.: Multitask Deep Reinforcement Learning with PopArt]
 MERLIN model [Greg Wayne et al.:Unsupervised Predictive Memory in a GoalDirected Agent]
 FTW agent for multiplayer CTF [Max Jaderberg et al.: Humanlevel performance in firstperson multiplayer games with populationbased deep reinforcement learning]
You should submit the assignments in the ReCodEx Code Examiner, where they will be either automatically or manually evaluated (depending on the assignment). The evaluation is performed using Python 3.6, TensorFlow 1.11.0, NumPy 1.15.2 and OpenAI Gym 0.9.5. For those using PyTorch, CPU version 1.0.0 is available.
You can install TensorFlow and Gym either to user packages using
pip3 install user tensorflow==1.11.0 gym==0.9.5 scipy box2dpy ataripy
(with the last three backages being optinal dependencies of gym
),
or create a virtual environment using python3 m venv VENV_DIR
and then installing
the packages inside it by running
VENV_DIR/bin/pip3 install tensorflow==1.11.0 gym==0.9.5 scipy box2dpy ataripy
.
On Windows, you can use thirdparty precompiled versions of
box2dpy
and ataripy.
Note that when your CPU does not support AVX, you need to install TensorFlow 1.5.
Submitting Data Files to ReCodEx
Even if ReCodEx allows submitting data files
beside Python sources, the data files are not available during evaluation.
Therefore, in order to submit models, you need to embed them in Python sources.
You can use the embed.py
script,
which compressed and embeds given files and directories into a Python module
providing an extract()
method.
Teamwork
Working in teams of size 2 (or at most 3) is encouraged. All members of the team must submit in ReCodEx individually, but can have exactly the same sources/models/results. However, each such solution must explicitly list all members of the team to allow plagiarism detection using this template.
multiarmed_bandits
Deadline: Oct 21, 23:59 compulsory
Perform a parameter study of various approaches to solving multiarmed bandits. For every hyperparameter choice, perform 1000 episodes, each consisting of 1000 trials, and report averaged return (a single number).
Start with the multiarmed_bandits.py
template, which defines MultiArmedBandits
environment. We use API based on
OpenAI Gym Environment
class, notably the following
two methods:
reset() → new_state
: starts a new episodestep(action) → new_state, reward, done, info
: perform the chosen action in the environment, returning the new state, obtained reward, a boolean flag indicating an end of episode, and additional environmentspecific information Of course, the states are not used by the multiarmed bandits (None
is returned).
Your goal is to implement the following modes of calculation. For each mode evaluate the performance given specified hyperparameters and plot the results for all modes together in a single graph.
greedy
: perform $ε$greedy search with parameterepsilon
, computing the value function using averaging. Plot results for $ε ∈ \{1/64, 1/32, 1/16, 1/8, 1/4\}$.greedy
andalpha
$≠0$: perform $ε$greedy search with parameterepsilon
and initial function estimate of 0, using fixed learning ratealpha
. Plot results for $α=0.15$ and $ε ∈ \{1/64, 1/32, 1/16, 1/8, 1/4\}$greedy
,alpha
$≠0$ andinitial
$≠0$: perform $ε$greedy search with parameterepsilon
, giveninitial
value as starting value function and fixed learning ratealpha
. Plot results forinitial
$=1$, $α=0.15$ and $ε ∈ \{1/128, 1/64, 1/32, 1/16\}$.ucb
: perform UCB search with confidence levelc
and computing the value function using averaging. Plot results for $c ∈ \{1/4, 1/2, 1, 2, 4\}$.gradient
: choose actions according to softmax distribution, updating the parameters using SGD to maximize expected reward. Plot results for $α ∈ \{1/16, 1/8, 1/4, 1/2\}$.
This task will be evaluated manually and you should submit the Python source and the generated graph.
policy_iteration
Deadline: Oct 28, 23:59 compulsory
Consider the following gridworld:
Start with policy_iteration.py, which implements the gridworld mechanics, by providing the following methods:
GridWorld.states
: return number of states (11
)GridWorld.actions
: return lists with labels of the actions (["↑", "→", "↓", "←"]
)GridWorld.step(state, action)
: return possible outcomes of performing theaction
in a givenstate
, as a list of triples containingprobability
: probability of the outcomereward
: reward of the outcomenew_state
: new state of the outcome
Implement policy iteration algorithm, with steps
steps of policy
evaluation/policy improvement. During policy evaluation, use the current value
function and perform iterations
applications of the Bellman equation.
Perform the policy evaluation synchronously (i.e., do not overwrite the current
value function when computing its improvement). Assume the initial policy is
“go North” and initial value function is zero.
After given number of steps and iterations, print the resulting value function and resulting policy. For example, the output after 4 steps and 4 iterations should be:
9.15→ 10.30→ 11.32→ 12.33↑
8.12↑ 3.35← 2.58←
6.95↑ 5.90← 4.66← 4.93↓
monte_carlo
Deadline: Oct 28, 23:59 compulsory
Solve the CartPolev1 environment environment from the OpenAI Gym using the Monte Carlo reinforcement learning algorithm.
Use the supplied cart_pole_evaluator.py module (depending on gym_evaluator.py) to interact with the discretized environment. The environment has the following methods and properties:
states
: number of states of the environmentactions
: number of actions of the environmentepisode
: number of the current episode (zerobased)reset(start_evaluate=False) → new_state
: starts a new episodestep(action) → new_state, reward, done, info
: perform the chosen action in the environment, returning the new state, obtained reward, a boolean flag indicating an end of episode, and additional environmentspecific informationrender()
: render current environment state
Once you finish training (which you indicate by passing start_evaluate=True
to reset
), your goal is to reach an average return of 490 during 100
evaluation episodes. Note that the environment prints your 100episode
average return each 10 episodes even during training.
You can start with the monte_carlo.py template, which parses several useful parameters, creates the environment and illustrates the overall usage.
During evaluation in ReCodEx, three different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.
q_learning
Deadline: Nov 04, 23:59 compulsory
Solve the MountainCarv0 environment environment from the OpenAI Gym using the Qlearning reinforcement learning algorithm. Note that this task does not require TensorFlow.
Use the supplied mountain_car_evaluator.py
module (depending on gym_evaluator.py)
to interact with the discretized environment. The environment
methods and properties are described in the monte_carlo
assignment.
Your goal is to reach an average reward of 150 during 100 evaluation episodes.
You can start with the q_learning.py template, which parses several useful parameters, creates the environment and illustrates the overall usage. Note that setting hyperparameters of Qlearning is a bit tricky – I usualy start with a larger value of $ε$ (like 0.2 or even 0.5) an then gradually decrease it to almost zero.
During evaluation in ReCodEx, three different random seeds will be employed, and you need to reach the required return on all of them. The time limit for each test is 5 minutes.
importance_sampling
Deadline: Nov 04, 23:59 compulsory
Using the FrozenLakev0 environment environment, implement Monte Carlo weighted importance sampling to estimate state value function $V$ of target policy, which uniformly chooses either action 1 (down) or action 2 (right), utilizing behaviour policy, which uniformly chooses among all four actions.
Start with the importance_sampling.py template, which creates the environment and generates episodes according to behaviour policy.
For $1000$ episodes, the output of your program should be the following:
0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00
0.00 0.00 0.21 0.00
0.00 0.00 0.45 0.00
lunar_lander
Deadline: Nov 11, 23:59 compulsory & 7 bonus
Solve the LunarLanderv2 environment environment from the OpenAI Gym. Note that this task does not require TensorFlow.
Use the supplied lunar_lander_evaluator.py
module (depending on gym_evaluator.py
to interact with the discretized environment. The environment
methods and properties are described in the monte_carlo
assignment,
but include one additional method:
expert_trajectory() → initial_state, trajectory
This method generates one expert trajectory and returns a pair ofinitial_state
andtrajectory
, wheretrajectory
is a list of the tripples (action, reward, next_state). You can use this method only during training, not during evaluation.
To pass the task, you need to reach an average return of 0 during 100 evaluation episodes. During evaluation in ReCodEx, three different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.
The task is additionally a competition and at most 7 points will be awarded according to relative ordering of your solution performances.
You can start with the lunar_lander.py template, which parses several useful parameters, creates the environment and illustrates the overall usage.
q_learning_tiles
Deadline: Nov 18, 23:59 compulsory
Improve the q_learning
task performance on the
MountainCarv0 environment
environment using linear function approximation with tile coding.
Your goal is to reach an average reward of 110 during 100 evaluation episodes.
Use the updated mountain_car_evaluator.py
module (depending on updated gym_evaluator.py)
to interact with the discretized environment. The environment
methods and properties are described in the monte_carlo
assignment, with the
following changes:

The
env.weights
method return the number of weights of the linear function approximation. 
The
state
returned by theenv.step
method is a list containing weight indices of the current state (i.e., the feature vector of the state consists of zeros and ones, and only the indices of the ones are returned). The (action)value function for a state is therefore approximated as a sum of the weights whose indices are returned byenv.step
.The default number of tiles in tile encoding (i.e., the size of the list with weight indices) is
args.tiles=8
, but you can use any number you want (but the assignment is solvable with 8).
You can start with the q_learning_tiles.py template, which parses several useful parameters, creates the environment and illustrates the overall usage. Implementing Qlearning is enough to pass the assignment, even if both Nstep Sarsa and Tree Backup converge a little faster.
During evaluation in ReCodEx, three different random seeds will be employed, and you need to reach the required return on all of them. The time limit for each test is 5 minutes.
q_network
Deadline: Nov 25, 23:59 compulsory
Solve the CartPolev1 environment environment from the OpenAI Gym using Qlearning with neural network as a function approximation.
The supplied cart_pole_evaluator.py
module (depending on gym_evaluator.py)
can also create a continuous environment using environment(discrete=False)
.
The continuous environment is very similar to the discrete environment, except
that the states are vectors of realvalued observations with shape environment.state_shape
.
Use Qlearning with neural network as a function approximation, which for a given states returns stateaction values for all actions. You can use any network architecture, but two hidden layers of 20 ReLU units are a good start.
Your goal is to reach an average return of 400 during 100 evaluation episodes.
You can start with the q_network.py template, which provides a simple network implementation in TensorFlow.
During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 10 minutes (so you can train in ReCodEx, but you can also pretrain your network if you like).
car_racing
Deadline: Dec 2, 23:59 10 bonus only
Nov 27: The evaluator has been returning a reference to the same numpy array with the state, which could have caused problems if you did not create a copy (when stacking images or resizing it). It has now been fixed.
In this bonusonly exercise to play with Deep Q Network and its variants, try solving the CarRacingv0 environment environment from the OpenAI Gym.
Use the supplied car_racing_evaluator.py module (depending on gym_evaluator.py to interact with the environment. The environment is continuous and states are RGB images of size $96×96×3$, but you can downsample them even more. The actions are also continuous and consist of an array with the following three elements:
steer
in range [1, 1]gas
in range [0, 1]brake
in range [0, 1]
Internally you should generate discrete actions and convert them to the required
representation before the step
call. Good initial action space is to use
9 actions – a Cartesian product of 3 steering actions (left/right/none) and
3 driving actions (gas/brake/none).
The environment supports frame skipping without
rendering the skipped frames – the second argument to env.step
determines
how many time is the given action repeated.
The task is a competition and at most 10 points will be awarded according to relative ordering of your solution performances. In ReCodEx, your solution is evaluated on 15 different tracks with a total time limit of 15 minutes. If your average return is at least 100, ReCodEx shows the solution as correct.
The car_racing.py template parses several useful parameters and creates the environment. Note that the car_racing_evaluator.py can be executed directly and in that case you can drive the car using arrows.
reinforce
Deadline: Dec 02, 23:59 compulsory
Solve the CartPolev1 environment environment from the OpenAI Gym using the REINFORCE algorithm.
The supplied cart_pole_evaluator.py
module (depending on gym_evaluator.py)
can create a continuous environment using environment(discrete=False)
.
The continuous environment is very similar to the discrete environment, except
that the states are vectors of realvalued observations with shape environment.state_shape
.
Your goal is to reach an average return of 490 during 100 evaluation episodes.
You can start with the reinforce.py template, which provides a simple network implementation in TensorFlow.
During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.
reinforce_with_baseline
Deadline: Dec 09, 23:59 compulsory
This is a continuation of reinforce
assignment.
Using the reinforce_with_baseline.py template, solve the CartPolev1 environment environment using the REINFORCE with baseline algorithm.
Using a baseline lowers the variance of the value function gradient estimator, which allows faster training and decreases sensitivity to hyperparameter values. To reflect this effect in ReCodEx, note that the evaluation phase will automatically start after 200 episodes. Using only 200 episodes for training in this setting is probably too little for the REINFORCE algorithm, but suffices for the variant with a baseline.
Your goal is to reach an average return of 490 during 100 evaluation episodes.
During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 5 minutes.
cart_pole_pixels
Deadline: Dec 09, 23:59 compulsory & 7 bonus
The supplied cart_pole_pixels_evaluator.py
module (depending on gym_evaluator.py)
generates a pixel representation of the CartPole
environment
as an $80×80$ image with three channels, with each channel representing one time step
(i.e., the current observation and the two previous ones).
To pass the compulsory part of the assignment, you need to reach an average return of 50 during 100 evaluation episodes. During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 10 minutes.
The task is additionally a competition and at most 7 points will be awarded according to relative ordering of your solution performances.
The cart_pole_pixels.py
template parses several parameters, creates the environment
and shows how to save and load neural networks in TensorFlow.
To upload the trained model to ReCodEx, you need to embed the
trained model files using embed.py,
submit the resulting embedded_data.py
along your solution, and
in your solution you need to import embedded_data
and then
embedded_data.extract()
(the template does this for you).
paac
Deadline: Dec 16, 23:59 compulsory
Using the paac.py template, solve the CartPolev1 environment environment using parallel actorcritic algorithm.
The gym_environment
now provides the following two methods:
parallel_init(num_workers) → initial_states
, which initializes the given number of parallel workers and returns their environment initial states. This method can be called at most once.parallel_step(actions) → List[next_state, reward, done, info]
, which performs given action in respective environment, and return the usual information with one exception: Ifdone=True
, thennext_state
is already a new state of newly started episode.
Your goal is to reach an average return of 450 during 100 evaluation episodes.
During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 10 minutes.
paac_continuous
Deadline: Dec 16, 23:59 compulsory
Using the paac_continuous.py template, solve the MountainCarContinuousv0 environment environment using parallel actorcritic algorithm with continuous actions.
The gym_environment
now provides two additional methods:
action_shape
: returns required shape of continuous action. You can assume the actions are always an onedimensional vector.action_ranges
: returns a pair of vectorslow
,high
. These denote valid ranges for the actions, solow[i]
$≤$action[i]
$≤$high[i]
.
Your goal is to reach an average return of 90 during 100 evaluation episodes.
During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 10 minutes.
ddpg
Deadline: Jan 06, 23:59 compulsory
Using the ddpg.py template, solve the Pendulumv0 environment environment using deep deterministic policy gradient algorithm.
To create the evaluator, use
gym_evaluator.py.GymEvaluator("Pendulumv0")
.
The environment is continuous, states and actions are described at
OpenAI Gym Wiki.
Your goal is to reach an average return of 200 during 100 evaluation episodes.
During evaluation in ReCodEx, two different random seeds will be employed, and you need to reach the required return on all of them. Time limit for each test is 10 minutes.
walker
Deadline: Jan 06, 23:59 10 bonus only
In this bonusonly exercise exploring continuous robot control, try solving the BipedalWalkerv2 environment environment from the OpenAI Gym.
To create the evaluator, use
gym_evaluator.py.GymEvaluator("BipedalWalkerv2")
.
The environment is continuous, states and actions are described at
OpenAI Gym Wiki.
The task is a competition and at most 10 points will be awarded according to relative ordering of your solution performances. In ReCodEx, your solution will be evaluated on 100 different tracks with a total time limit of 10 minutes. If your average return is at least 0, ReCodEx shows the solution as correct.
You can start with the ddpg.py
template, only set args.env
to BipedalWalkerv2
.
walker_hardcore
Deadline: Jan 06, 23:59 5 bonus only
As an extesnion of the walker
assignment, try solving the
BipedalWalkerHardcorev2 environment
environment from the OpenAI Gym.
The task is a competition and at most 5 points will be awarded according to relative ordering of your solution performances. In ReCodEx, your solution will be evaluated on 100 different tracks with a total time limit of 10 minutes. If your average return is at least 0, ReCodEx shows the solution as correct.
You can start with the ddpg.py
template, only set args.env
to BipedalWalkerHardcorev2
.
az_quiz
Deadline: Jan 13, 23:59 10 bonus only
In this bonusonly exercise, use Monte Carlo Tree Search to learn an agent for a simplified version of AZkvíz. In our version, the agent does not have to answer questions and we assume that all answers are correct.
The game itself is implemented in the
az_quiz.py
module, using randomized=False
constructor argument.
The evaluation in ReCodEx should be implemented by importing a module
az_quiz_evaluator_recodex
and calling its evaluate
function. The argument
this functions is an object providing a method play
which given an AZkvíz
instance returns the chosen move. The illustration of the interface is in the
az_quiz_evaluator_recodex.py
module.
Your solution in ReCodEx is automatically evaluated only against a random player az_quiz_player_random.py and a very simple heuristic az_quiz_player_simple_heuristic.py, playing against each of them 10 games as a starting player and 10 games as a nonstarting player. The time limit for the games is 10 minutes and you should see win rate directly in ReCodEx. The final evaluation will be performed after the deadline by a roundrobin tournament, utilizing your latest submission with nonzero win rate.
For inspiration, use the official pseudocode for AlphaZero.
az_quiz_randomized
Deadline: Jan 13, 23:59 5 bonus only
Extend the az_quiz
assignment to handle the possibility of wrong
answers. Therefore, when choosing a field, the agent might answer
incorrectly.
To instantiate this randomized game variant, pass randomized=True
to the AZQuiz
class of az_quiz.py.
The Monte Carlo Tree Search has to be slightly modified to handle stochastic
MDP. The information about distribution of possible next states is provided
by the AZQuiz.all_moves
method, which returns a list of (probability, az_quiz_instance)
next states (in our environment, there are always two
possible next states).
The evaluation in ReCodEx should be implemented by importing a module
az_quiz_evaluator_recodex
and calling its evaluate
function. The argument
this functions is an object providing a method play
which given an AZkvíz
instance returns the chosen move. The illustration of the interface is in the
az_quiz_evaluator_recodex.py
module.
Your solution in ReCodEx is automatically evaluated only against a random player az_quiz_player_random.py and a very simple heuristic az_quiz_player_simple_heuristic.py, playing against each of them 10 games as a starting player and 10 games as a nonstarting player. The time limit for the games is 10 minutes and you should see win rate directly in ReCodEx. The final evaluation will be performed after the deadline by a roundrobin tournament, utilizing your latest submission with nonzero win rate.
For inspiration, use the official pseudocode for AlphaZero.
vtrace
Deadline: Jan 20, 23:59 compulsory
Using the vtrace.py template, implement the Vtrace algorithm.
The template uses the CartPolev1
environment and a replay buffer to more
thoroughly test the offpolicy capability of the Vtrace algorithm.
However, the evaluation in ReCodEx will be performed by calling only the
vtrace
method and comparing its results to a reference implementation.
Several values of hyperparameters will be used, each test has a time limit
of 1 minute, and all tests must pass.
memory_game
Deadline: Feb 17, 23:59 5 bonus only
In this bonusonly exercise we explore a partially observable environment. Consider a oneplayer variant of a memory game (pexeso), where a player repeatedly flip cards. If the player flips two cards with the same symbol in succession, the cards are removed and the player recieves a reward of +2. Otherwise the player recieves a reward of 1. An episode ends when all cards are removed. For a given even $N$, there are $N$ actions – the card indices, and $N/2$ observations, which encode card symbol. Every episode can be ended using at most $3N/2$ actions. The environment is provided by the memory_game_evaluator.py module.
Your goal is to solve the environment using an agent utilizing a LSTM cell. The reinforce algorithm with baseline seems an appropriate choice.
ReCodEx evaluates your solution on environments with 4, 6, 8 and 16 cards
(utilizing the cards
argument). For each card number, 1000 episodes are
simulated and your solution gets 1 point (2 points for 16 cards) if the average
return is positive.
A template memory_game.py
is available. Depending on memory_cells
argument, it employs either a vanilla
LSTM or LSTM with external memory. Note that I was able to train it only for 4, 6,
and 8 cards.

Lecture 1: Introduction to Reinforcement Learning
 Multiarmed bandits [Chapter 2 of RLB]

Lecture 2: Markov Decision Process, Optimal Solutions, Monte Carlo Methods
 Markov Decision Process [Sections 33.3 of RLB]
 Policies and Value Functions [Sections 3.53.6 of RLB]
 Value Iteration [Sections 4 and 4.4 of RLB]
 Proof of convergence only in slides
 Policy Iteration [Sections 4.14.3 of RLB]
 Generalized Policy Iteration [Section 4.6 or RLB]
 Monte Carlo Methods [Sections 55.4 of RLB]

Lecture 3: Temporal Difference Methods, OffPolicy Methods
 Modelfree and modelbased methods, using statevalue or actionvalue functions [Chapter 8 before Section 8.1, and Section 6.8 of RLB]
 Temporaldifference methods [Sections 66.3 of RLB]
 Sarsa [Section 6.4 of RLB]
 Qlearning [Section 6.5 of RLB]
 Offpolicy Monte Carlo Methods [Sections 5.55.7 of RLB]
 Expected Sarsa [Section 6.6 of RLB]

Lecture 4: Nstep Methods, Function Approximation
 Double Qlearning [Section 6.7 of RLB]
 Nstep TD policy evaluation [Section 7.1 of RLB]
 Offpolicy nstep Sarsa [Section 7.3 of RLB]
 Tree backup algorithm [Section 7.5 of RLB]
 Function approximation [Sections 99.3 of RLB]
 Tile coding [Section 9.5.4 of RLB]

Lecture 5: Function Approximation, Deep Q Network
 Linear function approximation [Section 9.4 of RLB, without the Proof of Convergence if Linear TD(0)]
 SemiGradient TD methods [Sections 9.3, 1010.2 of RLB]
 Offpolicy function approximation TD divergence [Sections 11.211.3 of RLB]
 Deep Q Network [Volodymyr Mnih et al.: Humanlevel control through deep reinforcement learning]

Lecture 6: Rainbow
 Double Deep Q Network (DDQN) [Hado van Hasselt et al.: Deep Reinforcement Learning with Double Qlearning]
 Prioritized Experience Replay [Tom Schaul et al.: Prioritized Experience Replay]
 Dueling Deep Q Network [Ziyu Wang et al.: Dueling Network Architectures for Deep Reinforcement Learning]
 Noisy Nets [Meire Fortunato et al.: Noisy Networks for Exploration]
 Distributional Reinforcement Learning [Marc G. Bellemare et al.: A Distributional Perspective on Reinforcement Learning]
 Rainbow [Matteo Hessel et al.: Rainbow: Combining Improvements in Deep Reinforcement Learning]

Lecture 7: Policy Gradient Methods
 Policy Gradient Methods [Sections 1313.1 of RLB]
 Policy Gradient Theorem [Section 13.2 of RLB]
 REINFORCE algorithm [Section 13.3 of RLB]
 REINFORCE with baseline algorithm [Section 13.4 of RLB]
 ActorCritic methods [Section 13.5 of RLB, without the eligibility traces variant]

Lecture 8: Advantage ActorCritic, Continuous Action Space
 A3C and asynchronous RL [Volodymyr Mnih et al.: Asynchronous Methods for Deep Reinforcement Learning]
 PAAC [Alfredo V. Clemente et al.: Efficient Parallel Methods for Deep Reinforcement Learning]
 Gradient methods with continuous actions [Section 13.7 of RLB]

Lecture 9: Deterministic Policy Gradient, Advanced RL Algorithms
 Deterministic policy gradient theorem (DPG) [David Silver et al.: Deterministic Policy Gradient Algorithms]
 Deep deterministic policy gradient (DDPG) [Timothy P. Lillicrap et al.: Continuous Control with Deep Reinforcement Learning]

Lecture 10: TD3, Monte Carlo Tree Search
 Twin delayed deep deterministic policy gradient (TD3) [Scott Fujimoto et al.: Addressing Function Approximation Error in ActorCritic Methods]
 AlphaZero [David Silver et al.: A general reinforcement learning algorithm that masters chess, shogi, and Go through selfplay]

Lecture 11: Vtrace, PopArt Normalization, Partially Observable MDPs
 The Vtrace algorithm of IMPALA [Lasse Espeholt et al.: IMPALA: Scalable Distributed DeepRL with Importance Weighted ActorLearner Architectures]
 PopArt reward normalization [Matteo Hessel et al.: Multitask Deep Reinforcement Learning with PopArt]