P RIORITIZED L EVEL R EPLAY
Anonymous authors
Paper under double-blind review
A BSTRACT
Simulated environments with procedurally generated content have become popular benchmarks for testing systematic generalization of reinforcement learning agents.
Every level in such an environment is algorithmically created, thereby exhibiting a unique conﬁguration of underlying factors of variation, such as layout, positions of entities, asset appearances, or even the rules governing environment transitions.
Fixed sets of training levels can be determined to aid comparison and reproducibility, and test levels can be held out to evaluate the generalization and robustness of agents.
While prior work samples training levels in a direct way (e.g. uniformly) for the agent to learn from, we investigate the hypothesis that different levels provide different learning progress for an agent at speciﬁc times during training.
We introduce Prioritized Level Replay , a general framework for estimating the future learning potential of a level given the current state of the agent’s policy.
We ﬁnd that temporal-difference (TD) errors, while previously used to selectively sample past transitions, also prove effective for scoring a level’s future learning potential when the agent replays (that is, revisits) that level to generate entirely new episodes of experiences from it.
We report signiﬁcantly improved sample-efﬁciency and generalization on the majority of Procgen Benchmark environments as well as two challenging MiniGrid environments.
Lastly, we present a qualitative analysis showing that Prioritized Level Replay induces an implicit curriculum, taking the agent gradually from easier to harder levels.
I NTRODUCTION
Environments generated using procedural content generation (PCG) have garnered increasing interest in RL research, leading to a surge of PCG environments such as MiniGrid (Chevalier-Boisvert et al., 2018), the Obstacle Tower Challenge (Juliani et al., 2019), the Procgen Benchmark (Cobbe et al., 2019), and the NetHack Learning Environment (K¨uttler et al., 2020).
Unlike singleton environments, like those in the Arcade Learning Environment benchmark (Bellemare et al., 2013), which are exploitable by memorization and deterministic reset strategies (Ecoffet et al., 2019; 2020), PCG environments create novel environment instances or levels algorithmically.
Every such level exhibits a unique conﬁguration of underlying factors of variation, such as layout, positions of game entities, asset appearances, or even different rules governing environment transitions, making them a promising target for evaluating systematic generalization in RL (Risi & Togelius, 2020).
Each level can be associated with a level identiﬁer (e.g. an index, a random number generator seed, etc.) used by the PCG algorithm to generate a speciﬁc level.
This allows for a clean notion of train-test split and testing on held-out levels, in line with the best practices from supervised learning.
Due to the variation among algorithmically-generated levels, a random selection of PCG levels can, in principle, correspond to levels of varying difﬁculty as well as reveal different—perhaps rare—environment dynamics.
This diversity among levels implies that different levels hold differing learning potential for an agent at any point in training, a fact exploited by methods that both learn to generate levels as well as to solve them (Wang et al., 2019; 2020).
Here, we focus on the less intrusive setting where we do not have control over level generation, but can instead replay (that is, revisit) any previously visited level during training to generate entirely new experiences from it.
We introduce Prioritized Level Replay, illustrated in Figure 1, a method for sampling training levels that exploits this difference among levels.
Our method induces a level-sampling distribution that prioritizes levels based on the learning potential of replaying each level anew.
Throughout agent training, our method updates a heuristic score appraising the agent’s learning potential on a level
[Figure]
Rather than sampling from the default, typically static (e.g. uniform), training level distribution, our method samples from a distribution derived from a normalization procedure over these level scores.
Prioritized Level Replay does not make any assumption about how the policy is updated, and is therefore compatible with any RL method.
Furthermore, our method does not rely on some external or general method for quantifying difﬁculty of a level, but instead derives a level score directly from the policy.
The only requirements—satisﬁed in a number of settings where a simulator or game is used to collect experience—are as follows notion of “level” levels can be sampled from the environment in an identiﬁable way,
(iii given a level identiﬁer, it is possible to set the environment to that level to be able to collect new experiences from it.
While previous works in off-policy RL devised effective methods to directly reuse past experiences for training (Schaul et al., 2015; Andrychowicz et al., 2017), Prioritized Level Replay uses past experiences to inform the collection of future experiences by assessing how much replaying each level anew will beneﬁt learning.
Our method can thus be seen as a forward-view variation of prioritized experience replay, and an online counterpart to this off-policy method for policy-gradient algorithms.
This paper makes the following core contributions: (i) we introduce a computationally efﬁcient algorithm for adaptively prioritizing levels throughout training using a heuristic-based assessment of the learning potential of replaying each level, (ii) we empirically demonstrate our method leads to signiﬁcant gains on 11 of 16 Procgen Benchmark environments and two challenging MiniGrid environments, (iii) we demonstrate our method combines with a previous leading method to set a new state-of-the-art on Procgen Benchmark, and (iv) we provide evidence that our method induces an implicit curriculum over training levels in sparse reward settings.
B ACKGROUND
In this paper, we refer to a PCG environment as any computational process that, given a level identiﬁer (e.g. an index or a seed), generates a level , deﬁned as an environment instance exhibiting a unique conﬁguration of its underlying factors of variation, such as layout, positions of game entities, asset appearances, or even rules that govern the environment transitions (Risi & Togelius, 2020).
For example, MiniGrid’s MultiRoom environment instantiates mazes with varying numbers of rooms based on the seed (Chevalier-Boisvert et al., 2018).
We refer to sampling a new trajectory generated from the agent’s latest policy acting on a given level l as replaying that level l .
The level diversity of PCG environments makes them useful testbeds for studying the robustness and generalization ability of RL agents, measured by agent performance on unseen test levels.
The standard test evaluation protocol for PCG environments consists of training the agent on a ﬁnite number of training levels, Λ train and evaluating performance on unseen test levels Λ test , drawn from the set of all levels.
A common variation of this protocol sets Λ train to the set of all levels, though the agent will still effectively only sees a ﬁnite set of levels during training.
Training levels are sampled from an arbitrary distribution P train ( l | Λ train ) .
We call this training process direct level sampling .
In practice, for the case of a ﬁnite training set, typically P train ( l | Λ train ) =
Uniform ( l ; Λ train ) .
See
Appendix D for the pseudocode outlining this procedure.
In generating levels of varying difﬁculty, PCG environments naturally lend themselves to the study of curriculum-based training protocols.
For example, Justesen et al.
(2018) and Wang et al.
(2019; 2020) show that generating progressively harder levels improves generalization of agents across difﬁculty settings, demonstrating that the set of levels most useful for improving an agent’s performance varies throughout the course of training.
When levels cannot directly be generated, many previous works have exploited handcrafted curricula to learn policies on harder levels (ChevalierBoisvert et al., 2018; Zhong et al., 2020).
Instead, we are interested in answering how an effective curriculum over an existing set Λ train can be automatically discovered.
P RIORITIZED L EVEL R EPLAY
In this section, we present Prioritized Level Replay, a level-sampling algorithm that exploits differences in the learning potential for the current agent in replaying previously visited levels.
It is a drop-in replacement for the experience-collection process used in a wide range of RL algorithms.
To exemplify this, we show how it is straight-forward to incorporate Prioritized Level Replay into a generic policy-gradient training loop, as described in Algorithm 1 where, for clarity, we focus on training on batches of complete trajectories (see Appendix D for detailed pseudocode on how our procedure works with T -step rollouts).
Our method, illustrated in Figure 1 and fully speciﬁed in Algorithm 2, induces a dynamic and nonparametric sampling distribution P replay ( l | Λ seen ) over previously visited training levels Λ seen that prioritizes visited levels with higher learning potential based on properties of the agent’s past trajectories.
We refer to P replay ( l | Λ seen ) as the replay distribution .
Throughout an agent’s training, our method updates this replay distribution adaptively according to a heuristic score, assigning greater weight to visited levels with higher future learning potential.
Through variable-size lists S and C of equal length to Λ seen , Prioritized Level Replay tracks level scores S i ∈ S for each visited training level l i based on the latest episode trajectory on l i , as well as the episode count C i ∈ C at which each level l i ∈ Λ seen was last sampled.
Our method updates P replay after each terminated episode by computing a mixture of two distributions, P S , based on the level scores, and P C , based on how long ago each level was last sampled:
[Equation]
where the staleness coefﬁcient ρ ∈ [0 , 1] is a hyperparameter.
We discuss how we compute level scores ( S i ) parameterizing the scoring ( P S ) distribution, and the staleness ( P C ) distribution, in Sections 3.1 and 3.2, respectively.
Prioritized Level Replay chooses the next level at the start of every training episode by ﬁrst sampling a replay-decision from a Bernoulli (or similar) distribution P D ( d ) to determine whether to replay a level sampled from the replay distribution P replay ( l | Λ seen ) or to experience a new, unseen level from Λ train , according to some distribution P new ( l | Λ train \ Λ seen ) .
In practice, for the case of a ﬁnite number of training levels, we implement P new as a uniform distribution over the remaining unseen levels.
For the case of a countably inﬁnite number of training levels, we simulate P new by sampling levels from
P train until encountering an unseen level.
In our experiments based on a ﬁnite number of training levels, we opt to perform a “warm start” by setting P D ( d = 0)
=
1 (i.e. sampling a new level) as long as | Λ train \ Λ seen | > 0 (i.e. not every level has been seen), ensuring the replay distribution takes into account scores from at least one trajectory from each level.
The following sections describe the main steps of how Prioritized Level Replay updates the replay distribution P replay ( l | Λ seen ) , namely level scoring and staleness-aware prioritization.
3. S CORING LEVELS FOR LEARNING POTENTIAL
After collecting each full trajectory τ over a completed episode on level l i using policy π , our method assigns l i a score S i = score ( τ, π ) measuring the learning potential of replaying l i in the future.
We employ a function of the TD-error at timestep t , δ t =
r t + γV
( s t +1 )
− V ( s t ) , as a proxy
Sample replay decision d ∼ P D ( d ) if d = 0 and | Λ train \ Λ seen | > 0 then Deﬁne new index i
| S + Sample l i ∼ P new ( l Λ train
Λ seen )
Add l i to Λ add initial value 0 to C else
Sample l i
P seen
S
+ ρ · P
C end if
Sample P
) Update score S score ( τ timestamp C i ← c for this learning potential. Its expectation over next states is equivalent to the advantage estimate, and therefore higher-magnitude TD-errors imply greater discrepancy between expected and actual returns, making δ t a useful measure of the learning potential in revisiting a particular state transition.
To prioritize the learning potential of future experiences resulting from replaying a level, we use a scoring function based on the average magnitude of the Generalized Advantage Estimate (GAE;
Schulman et al., 2015) over each of T time steps in the latest trajectory τ from that level:
[Equation]
Importantly, while the GAE at time t is most commonly expressed as the discounted sum of all 1 -step TD-errors starting at t as in Equation 2, it is equivalent to an exponentially-discounted sum of all k -step TD-errors from t , with discount factor λ .
By considering all k -step TD-errors, the GAE mitigates the bias introduced by the bootstrap term in 1 -step TD-errors.
The discount factor λ then controls the trade-off between bias and variance.
Our scoring function considers the absolute value of the GAE, as we assume the degree of learning potential grows with the magnitude of the TD-error irrespective of its sign.
Another useful interpretation of Equation 2 comes from observing that the GAE at t is equivalent to the L1 value loss | ˆ V t − V t | when applied in the context of a policy-gradient algorithm that uses
GAE for its own advantage estimates—and therefore value targets ˆ V t —as done in state-of-the-art implementations of PPO (Schulman et al., 2017) used in our experiments.
For this reason, we will refer to this instantiation of our algorithm as value-based level replay .
While we provide principled motivations for our speciﬁc choice of scoring function, we emphasize that in general, the scoring function can be any approximation of learning potential based on pertrajectory values.
In Section 4, we will compare our choice of the unsigned GAE, or equivalently, the L1 value loss, to alternative TD-error-based and uncertainty-based scoring approaches listed in
Table 1.
Given level scores, the normalized outputs of a prioritization function h evaluated over these scores and tuned using a temperature parameter β deﬁnes the score-prioritized distribution P S (Λ train ) over the training levels, under which
[Equation]
The function h deﬁnes how differences in level scores translate into differences in prioritization.
The temperature parameter β allows us to tune how much h ( S ) ultimately determines the resulting distribution.
We make the design choice of using rank prioritization, for which h ( S i ) = 1 / rank ( S i ) , where rank ( S i ) is the rank of level score S i among all scores sorted in descending order.
We also experimented with proportional prioritization ( h ( S i )
=
S i ), which performs comparably to rank prioritization, as well as greedy prioritization (the level with the highest score receives probability 1 ), which tends to perform worse.
3. S TALENESS -A WARE P RIORITIZATION
As the scores used to parameterize P S are a function of the state of the policy at the time the associated level was last played, they come to reﬂect a gradually more off-policy measure the longer they remain without an update through replay.
We mitigate this drift towards “off-policy-ness” by explicitly mixing the sampling distribution with a staleness-prioritized distribution P C
[Equation]
which assigns probability mass to each level l i in proportion to the level’s staleness c − C i .
Here, c is the count of total episodes sampled so far in training and C i (referred to as the level’s timestamp) is the episode count at which l i was last sampled.
By pushing support to levels with staler scores,
P C ensures no score drifts too far off-policy.
Plugging Equations 3 and 4 into Equation 1 gives us a replay distribution that is calculated as
[Equation]
Thus, a level has an increased chance of being sampled when its score is high or it has not been sampled for a long time.
E XPERIMENTAL S ETTING
We evaluate the effectiveness of Prioritized Level Replay on several PCG environments with various implementations of scoring functions and prioritization schemes, and compare against the most common direct level sampling baseline for which P train ( l | Λ train ) =
Uniform ( l ; Λ train ) .
Speciﬁcally, we train and test on all 16 games in the Procgen Benchmark on the easy difﬁculty setting (Cobbe et al., 2019) and select hard exploration environments from the MiniGrid suite (Chevalier-Boisvert et al., 2018).
In addition, we demonstrate that our method, when combined with UCB-DrAC (Raileanu et al., 2020), sets a new state-of-the-art on Procgen Benchmark.
In line with the standard baseline for these environments, all our experiments use PPO with GAE for training.
For Procgen Benchmark we use the same ResBlock architecture as Cobbe et al.
and the 200 training levels used in the original baselines, while sampling unseen levels for every test run.
For MiniGrid, we use a 3-layer CNN architecture similar to that in Igl et al.
(2019), and provide approximatelylevels of each difﬁculty for every environment during training.
Detailed descriptions of the environments, architectures, and hyperparameters used in our experiments (as well as how they were set or obtained) can be found in Appendices A and B. See Table 1 for the full set of scoring functions score ( τ, π ) investigated in our experiments.
Additionally, we report results of training on the full level distribution of select MiniGrid levels in Appendix C.2.
[Table]
[Figure]
M ean t e s t
ep i s ode r e t u r n
R ESULTS AND D ISCUSSION
Prioritized Level Replay consistently provides better (or at least on-par) results than direct level sampling.
In particular, we ﬁnd that: (i) value-based level replay consistently outperforms direct level sampling in generalization and sample-efﬁciency across environments, (ii) alternative scoring functions lead to inconsistent improvements across environments, (iii) our method can yield signiﬁcant additive gains combined with other methods, and (iv) value-based level replay induces an implicit curriculum over training levels, which substantially aids training in hard exploration environments.
5. P ROCGEN B ENCHMARK
Our results in Figure 2 show that value-based level replay combined with rank prioritization, with β = 0 .
1 and ρ = 0 . 1 , leads to the most drastic improvements across games.
This setting yields statistically signiﬁcant ( p = 0 .
05 ) improvements in generalization performance over uniform-sampling on 10 out of 16 environments, according to Welch’s t-test.
In comparison, value-based level replay with proportional prioritization provides statistically signiﬁcant gains over uniform sampling on one more game, but on average sees worse mean test returns.
Notably, gains only occur when the replay distribution considers both level scores and staleness, highlighting the importance of staleness-based sampling in keeping scores from drifting off-policy.
Further, we report considerable improvements over uniform level-sampling on the hard setting, when directly using the best hyperparameters found for the easy setting.
Figures 8, 9, 11 and Table 4 in Appendix C report these additional results. [Figure]
t e s t ep i s ode r e t u r n The remaining scoring metrics based on TD-error and classiﬁer uncertainty perform inconsistently across games.
While certain games such as BigFish seem amenable to improvements in both training sample complexity and generalization under various Prioritized Level Replay scoring metrics, other games, such as Ninja, see no improvement or even degraded sample complexity and generalization.
See Figure 3 for an example of this inconsistent effect across games.
We conjecture that sampling levels with Prioritized Level Replay under value-based replay induces an implicit curriculum over the training levels.
Easier levels result in non-zero, non-stationary returns earlier in training, while harder levels give rise to near stationary returns until the agent learns an improved policy that allows making further progress on the level.
Therefore, meaningful nonstationary value regression targets appear earlier in easier levels than in harder levels, leading to higher value losses on easier levels than on harder levels, where the agent can not yet make progress.
Sampling levels according to the L1 value-loss then leads to an implicit curriculum from easier to harder levels.
In the next section, we test this conjecture in hard exploration environments in MiniGrid, whose levels are clearly separated into discrete, qualitatively different difﬁculties.
Prioritized Level Replay easily combines with most other methods, as it simply deﬁnes a process for adapting the training level-sampling distribution as a function of the agent’s past trajectories and policy instantiations during those trajectories.
To demonstrate this fact, we combine our method with UCB-DrAC (Raileanu et al., 2020).
At the time of writing, UCB-DrAC had attained stateof-the-art results on the Procgen Benchmark.
Our combined setup trains agents using UCB-DrAC while sampling training levels at the start of each episode according to value-based level replay.
As summarized in Table 2, UCB-DrAC combined with our method sets a new state-of-the-art on the
Procgen Benchmark. Figure 2 showcases the test performance throughout training of this combined approach. The full details of this experiment are provided in Appendix C.3. 5. M INI G RID
As in the case of the Procgen Benchmark, we ﬁnd that value-based level replay with rank prioritization signiﬁcantly improves the sample efﬁciency and generalization performance in both environments.
On these MiniGrid environments, we ﬁnd a slightly higher staleness coefﬁcient of ρ = 0 .leads to better test performance.
The results are summarized in the top row of Figure 4.
For evaluation purposes, we bin each level into its corresponding difﬁculty, expressed as ascending, discrete values (note that Prioritized Level Replay does not have access to this privileged information).
From Figure 4 we see how the expected difﬁculty of levels sampled using value-based level replay changes throughout training for each environment.
We observe that as level weights are updated based on the agent’s past experiences of each level, these levels become sampled according to an implicit curriculum over the training levels that prioritizes progressively harder levels.
Notably, value-based level replay struggles to discover a useful curriculum for around the ﬁrst 4 ,updates on ObstructedMazeGamut-Medium, at which point it discovers a curriculum that gradually assigns more weight to harder levels.
This curriculum appears quite effective, enabling value-based level replay with access to only 6 , 000 training levels to attain even higher mean test returns than the uniform-sampling baseline with access to the full set of training levels, of which there are roughlybillion by our [Table] [Table]
[Figure]
R ELATED W ORK
Prior work for improving generalization in deep RL adapt techniques from supervised learning, including stochastic regularization (Igl et al., 2019; Cobbe et al., 2019), data augmentation (Kostrikov et al., 2020; Raileanu et al., 2020), and knowledge distillation (Igl et al., 2020; Cobbe et al., 2020).
In contrast, Prioritized Level Replay requires no modiﬁcations to the model architecture or training algorithm—it only modiﬁes the order in which rollouts are sampled from a collection of training levels.
Our method is compatible with any model or policy-gradient algorithm, and can be implemented alongside these approaches for potentially orthogonal gains.
Prioritized Level Replay is related to active learning (Cohn et al., 1994; Settles, 2009) and echoes ideas from Graves et al.
(2017), which uses gradient-based progress signals for training an adversarial multi-armed bandit that selectively samples the next task in multi-task supervised learning.
Sharma et al.
(2018) extend these ideas to multi-task RL, but add the additional requirement of knowing a maximum target return for each task a priori.
More recently, Zhang et al.
(2020) use an ensemble of value functions for selective goal sampling in the off-policy continuous control setting, requiring prior knowledge of the environment structure to generate candidate goals.
Notably, these methods depend on the ability to sample instances of speciﬁc similarly-structured tasks, an assumption that breaks in the PCG setting.
Rather, we show that our method automatically uncovers similarly difﬁcult levels, inducing a curriculum without any prior knowledge of the environment.
A recent theme in automated curriculum learning explores adaptively modifying the environment to facilitate learning.
Khalifa et al.
(2020) and Wang et al.
(2019; 2020) introduce methods that jointly generate levels and train agents to solve them, leading to a co-evolving curriculum.
Like
Prioritized Level Replay, such curricula exploit the changing differences in learning potential across levels throughout training.
Unlike these approaches, our method does not assume control over level generation, requiring only the ability to replay previously visited levels, as referenced by a level identiﬁer such as the corresponding seed used by the PCG algorithm to generate the level.
A related set of approaches makes use of multi-agent RL to train a separate teacher agent, which learns a goal or task proposal policy (Matiisen et al., 2020; Campero et al., 2020; Sukhbaatar et al., 2017).
In contrast to such teacher-student auto-curriculum methods, our approach relies only on a single agent, and therefore does not require extending the environment to accommodate additional agents, such as including teacher-speciﬁc action spaces.
Like our method, Schaul et al. (2015) uses TD-errors as a signal for learning potential.
However, while their method only scores 1 -step TD-errors to assess the value of revisiting individual past transitions, our method uses estimators based on k -step TD errors to predict the relative value of different choices for generating and training on entire trajectories of future experiences.
C ONCLUSION AND F UTURE W ORK
We introduced Prioritized Level Replay, a method for improving sample-efﬁciency and generalization performance of RL agents trained and evaluated on procedurally generated environments.
Prioritized Level Replay updates level sampling weights according to a scoring metric throughout the course of training.
We surveyed the effectiveness of several variations of our method to ﬁnd that L1 value-loss scores consistently lead to statistically signiﬁcant improvements in sample efﬁciency and generalization on a wide variety of PCG games, including the majority of environments in the ProcGen Benchmark and two challenging MiniGrid environments. Our method combines with a prior leading method to improve the state-of-the-art on Procgen Benchmark.
Further, on MiniGrid environments, we observe that our method induces an implicit curriculum over the training levels.
While we evaluated Prioritized Level Replay on PCG environments, we believe our method may be generally applicable to any RL environment, given a procedure for generating novel variations of the underlying MDP as a function of a level identiﬁer, for example by varying the starting positions of game entities.
Furthermore, as our method modiﬁes only how levels are sampled per episode, it combines easily with any on-policy RL algorithm, as well as other methods for improving sample complexity and generalization, potentially leading to additive gains.
Lastly, the scoring metric can be more generally parameterized as a learned function of the level and statistics of past trajectories over that level, which may prove even more effective than the L1 value-loss.
We plan to investigate these promising directions in future work.
A E XPERIMENT D ETAILS A. P ROC G EN B ENCHMARK
The OpenAI ProcGen Benchmark suite consists of 16 procedurally-generated games of varying styles, exhibiting a diversity of gameplay similar to that of the ALE benchmark.
Levels in each game are determined by a seed, and can vary in many ways, including layout, visual appearance, and starting positions of game entities.
All ProcGen environments share the same discrete 15dimensional action space and produce 64 × 64 × 3 RGB observations. Cobbe et al.
(2019) provides a comprehensive description of each of the 16 environments.
We follow the original protocol for testing generalization performance on ProcGen deﬁned by Cobbe et al.
(2019): We train an agent for each game on a ﬁnite number of levels, N train , and test on the full distribution of levels.
Previous benchmarks showed that training under this protocol using state-of-the-art policy-gradient algorithms leads to a signiﬁcant generalization gap between test and train performance in all games, making ProcGen a useful benchmark for assessing generalization performance.
To make the most efﬁcient use of our computational resources, we train on the easy difﬁculty mode using the recommended settings of N train = 200 and 25M steps of training, as well as the same
ResNet policy architecture and PPO hyperparameters shared across all games as Cobbe et al.
We ﬁnd 25M steps to be sufﬁcient for uncovering differences in generalization performance among our methods and standard baselines.
Moreover, under this setup, we ﬁnd ProcGen training runs require much less wall-clock time than training runs on our MiniGrid environments of interest over an equivalent number of steps needed to uncover differences in generalization performance.
Therefore we perform a survey of the empirical differences of various settings of Prioritized Level Replay on
ProcGen rather than MiniGrid.
We evaluate each combination of the scoring function choices in Table 1 with both rank and proportional prioritization, performing a coarse grid search for each pair over different settings of the temperature parameter β in { 0 . 1 , 0 . 5 , 1 . 0 , 1 . 4 , 2 . 0 } and the staleness coefﬁcient ρ in { 0 . 1 , 0 . 3 , 1 . 0 } .
For each setting, we run 4 trials across all 16 of games of the Procgen Benchmark.
We ﬁnd that rank prioritization tends to produce larger improvements to test performance than proportional prioritization, and the best setting across Procgen games was β = 0 .
1 , ρ = 0 .
In our TD-error-based scoring functions, we set γ and λ equal to the same respective values used by the GAE in PPO during training.
See Appendix B for an comprehensive overview of the same hyperparameter setting we used for PPO across all Procgen environments.
A. M INI G RID
In order to validate the conjecture that Prioritized Level Replay with L1 value-loss scores induces an implicit curriculum over the training levels, we turn to the MiniGrid suite (Chevalier-Boisvert et al., 2018).
MiniGrid features a series of highly structured environments of increasing difﬁculty.
Each environment features a task in a gridworld setting, and as in ProcGen, environment levels are determined by a seed.
Harder levels require the agent to perform longer action sequences over a combinatorially-rich set of game entities, on increasingly larger grids.
The clear ordering of difﬁculty over subsets of MiniGrid environments allows us to track the relative difﬁculty of levels sampled under Prioritized Level Replay over the course of training.
MiniGrid environments share a discrete 7-dimensional action space and produce a 3-channel integer state encoding of the 7 × 7 grid immediately including and in front of the agent.
However, following the training setup in Igl et al.
(2019), we modify the environment to produce an N × M × 3 encoding of the full grid, where N and M vary according to the maximum grid dimensions of each environment.
This fully observable setup requires the agent to generalize across different level layouts.
We evaluate value-based level replay with rank prioritization on two distinct MiniGrid environments, whose levels are uniformly distributed across several difﬁculty settings.
Training on levels of varying difﬁculties helps agents make use of the easier levels as stepping stones to learn useful behaviors that help the agent make progress on harder levels.
However, under the uniform-sampling baseline, learning may be inefﬁcient, as the training process does not selectively train the agent on levels of increasing difﬁculty, leading to wasted training steps when a difﬁcult level is sampled early in training.
On the contrary, if value-based level replay scores samples levels according to the time-averaged L1 value loss of recently experienced level trajectories, the average difﬁculty of the sampled levels should increase over time, following the reasoning outlined in 5.1, that signiﬁcant non-stationary value targets will be encountered earlier in easier levels.
As in Igl et al.
(2019), we parameterize the agent policy as a 3-layer CNN with 16, 32, andchannels.
with a ﬁnal hidden layer of size 64.
All kernels are 2 × 2 and use a stride of 1.
For the
ObstructedMazeGamut environments, we increase the number of channels of the ﬁnal CNN layer to 64.
We follow the same generalization evaluation protocol as used for ProcGen, training the agent on a ﬁxed set of 4000 levels for MultiRoom-N4-Random, 3000 levels for ObstructedMazeGamutEasy, and 6000 levels for ObstructedMazeGamut-Medium, and testing on the full level distribution.
We chose these values for Λ train to ensure roughly 1000 training levels of each represented difﬁculty setting of each environment.
As in the case of our Procgen experiments, we we set γ and λ equal to the same respective values used by the GAE in PPO during training, and use reward normalization during training. All MiniGrid experiments shared the same hyperparameters listed in B and, when using value-based level replay, set β = 0 . 1 , ρ = 0 . 3 with rank prioritization.
The remainder of this section provides more details about the various MiniGrid environments used in this work.
MultiRoom-N4-Random This environment requires the agent to navigate through 1, 2, 3, orrooms respectively to reach a goal object, resulting in a natural ordering of levels over four levels of difﬁculty. The agent always starts at a random position in the furthest room from the goal object, facing a random direction. The goal object is also initialized to a random position within its room.
See Figure 5 for screenshots of example levels.
ObstructedMaze-Gamut-Easy
This environment consists of levels uniformly distributed across the ﬁrst three difﬁculty settings of the ObstructedMaze environment, in which the agent must locate and pick-up the key in order to unlock the door to pick-up a goal object in a second room.
The agent and goal object are always initialized in random positions in different rooms separated by the locked door.
The second difﬁculty setting further requires the agent to ﬁrst uncover the key from under a box before picking up the key.
The third difﬁculty level further requires the agent to ﬁrst move a ball blocking the door before entering the door.
See Figure 6 for screenshots of example levels.
ObstructedMaze-Gamut-Hard
This environment consists of levels uniformly distributed across the ﬁrst six difﬁculty levels of the ObstructedMaze environment.
Harder levels corresponding to the fourth, ﬁfth, and sixth difﬁculty settings include two additional rooms with no goal object to distract the agent.
Each instance of these harder levels also contain two pairs of keys of different colors, each opening a door of the same color.
The agent always starts one room away from the randomly positioned goal object.
Each of the two keys is visible in the fourth difﬁculty setting and doors are unobstructed.
The ﬁfth difﬁculty setting hides the keys under boxes, and the sixth again places obstacles that must be removed before entering two of the doors, one of which is always the door to the goal-containing room.
See Figure 7 for example screenshots.
B H YPERPARAMETERS
Table 3 lists the hyperparameters used during training.
The values in each column were shared across all environments in Procgen and MiniGrid respectively.
For Procgen, these are the defaults used by Cobbe et al.
We adapted these for Minigrid, but did no further grid-searching of these values.
[Table]
C A DDITIONAL E XPERIMENTAL R ESULTS C. E XTENDED R ESULTS ON P ROCGEN B ENCHMARK
We show in Figures 8 and 9, the mean test episodic returns on the Procgen Benchmark for valuebased level replay with rank and proportional prioritization, respectively.
In both of these plots, we can see that using only staleness ( ρ = 1 ) or only L1 value loss scores ( ρ = 0 ) is considerably worse than direct level sampling.
Thus, we only observe gains compared to the baseline when both level scores and staleness are used for the sampling distribution.
Comparing Figures 8 with 9 we ﬁnd that value-based level replay with proportional instead of rank prioritization provides more statistically signiﬁcant gains compared to direct level sampling, but on average leads to worse mean test returns than rank prioritization.
Lastly, we show in Figure 10 that when value-based level replay improves generalization performance on a game, it also either matches or improves training sample efﬁciency.
This shows that when beneﬁcial to test-time performance, the representations learned via the auto-curriculum induced by our method prove similarly useful on the training levels.
However we see that our method reduces training sample efﬁciency on two games on which our method does not improve generalization performance.
Since our method does not discover useful auto-curricula for these games, it is likely that uniformly sampling levels at training time allows the agent to better memorize useful behaviors on each of the training levels compared to the selective sampling performed by our method.
[Figure]
M ean t e s t
ep i s ode r e t u r n
[Figure]
{+bigfish     bossfight 135 caveflyer 1357chaser 
+} [Figure] {+coinrun     dodgeball -
+}
[Figure]
{+heist 
+}
[Figure] [Figure]
[Figure] [Figure]
L1 value loss + proportional prioritization for various vs uniform-sampling baseline
[Figure]
M ean t e s t
ep i s ode r e t u r n
[Figure]
ean t r a i n ep
i s ode r e t u r n
Training sample [-efficiency of value-based level replay vs uniform-sampling baseline 0510 bigfish      bossfight    caveflyer    chaser      climber     coinrun 024 dodgeball -551525fruitbot 246 heist    jumper    leaper 357 maze 
-][Figure] value
[Figure]
based level replay
[Figure]
uniform-sampling baseline
[Figure]
[Figure]
[Figure]
[Figure]
[Figure]
[Table]
[Figure]
{+[Figure] 0246caveflyer 
 [Figure] climber 135 coinrun 
+}
[Figure]
heist
[Figure]
[Figure]
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C. T RAINING ON THE F ULL L EVEL D ISTRIBUTION
While assessment of generalization performance calls for using a ﬁxed set of training levels, ideally our method can also make use of the full level distribution if given access to it.
To take advantage of an unspeciﬁed number of training levels sampled from the full level distribution, we modify the list structures for storing scores and timestamps (see Algorithm 1 and 2) to keep up to a maximum of level
buffer size, M , items.
When the lists are full, we set the next level for replacement to be
[Equation]
When the outcome of the Bernoulli P D entails sampling from the replay distribution, resulting in sampling a new level l , the score and timestamps of l replace those of l min only if the score of l min is lower than that of l .
In this way, Prioritized Level Replay keeps a running buffer throughout training of the top M levels appraised to have the highest learning potential for replaying anew.
Figure 12 shows that with access to the full level distribution at training, value-based level replay improves sample-efﬁciency and generalization performance in both environments compared to uniform sampling.
In MultiRoom-N4-Random, the value M makes little difference to generalization performance, and access to the full level distribution at training leads to a policy outperforming one trained with value-based level replay on a ﬁxed set of training levels.
However, on ObstructedMazeGamutEasy, a smaller M leads to worse test performance, while training on a ﬁxed set of 3000 levels yields comparable generalization performance to a policy trained with value-based level replay on the full level distribution.
These results conﬁrm that for certain environments, the auto-curriculum discovered by our method leads to more sample-efﬁcient training on a ﬁxed set of levels than even training on the full level distribution.
[Figure]
M ean t e s t
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M ean t e s t
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C. O RTHOGONAL G AINS WITH UCB-D R AC
UCB-AutoDrAC, introduced in Raileanu et al.
(2020), has been shown to improve the generalization of PPO agents on Procgen Benchmark by making use of a data augmentation strategy.
This method extends PPO by introducing a UCB bandit trained concurrently with the agent.
Given a set of predeﬁned image transformations, F , this bandit learns to select, at the start of each PPO update, a single transformation to apply to every frame in the update.
Following the standard UCB algorithm, the selection rule for data augmentation f t before the t -th update is deﬁned as
[Equation]
where Q ( f ) is computed as a sliding window of the most recent K mean returns obtained by the agent after the policy was updated on frames transformed by f , the constant c is the UCB exploration constant, and N ( f ) counts how many times transformation f has been selected.
Note that the UCBAutoDrAC algorithm also introduces an additional KL-divergence term in the loss function to ensure the policy exhibits transformation-invariant optimality.
We refer the reader to Raileanu et al.
(2020) for the full description of this method.
We use the best hyperparameters for Procgen Benchmark reported in Raileanu et al.
(2020), setting
K = 10 and c = 0 .
For Prioritized Level Replay, we make use of the L1-value-loss scoring function with β = ρ = 0 .
Figure 13 shows test performance throughout training of UCB-AutoDrAC with L1-value-loss level replay against UCB-DrAC without level replay and PPO with uniform sampling.
{+051015bigfish     bossfight 135 caveflyer    chaser 
+}
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t [Figure] s t ep i s ode r
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t u
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n
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D A LGORITHMS
In this section, we provide detailed pseudocode for how Prioritized Level Replay can be used for experience collection with a policy-gradient method based on T -step rollouts.
Algorithm 3 presents the extension of the generic policy-gradient training loop presented in Algorithm 1 to the case of
T -step rollouts, and Algorithm 4 presents an implementation of experience collection in this setting (extending Algorithm 2).
Note that when using T -step rollouts in the training loop, rollouts may start and end between episode boundaries.
To compute level scores on full trajectories segmented across rollouts, we compute scores of partial episodes according to Equation 2, and record these partial scores alongside the partial episode step count in a separate buffer ˜ S .
The function S then technically takes ˜ S as an additional argument to combine the partial score with the score computed on the ﬁnal trajectory segment.
Initialize
C
Initialize global episode count c ←
Initialize set of visited levels Λ seen = ∅
Initialize experience buffer B = ∅
Initialize N b parallel environment instances E to a random level in ∈ Λ train for u = 1 to N u do
B ← ∅ for k = 1 to N b do
B ← B ∪ collect experiences ( k, E, Λ train , Λ seen , π θ , T S, ˜ S C, c
Using Algorithmend for θ ← U
B
) end for
Algorithm
Input: Actor index k , batch environments E , training levels Λ train , visited levels Λ seen , current level l , policy π θ , rollout length T , scoring function score , scores S partial scores ˜ S , staleness values C global episode count c .
Output: Experience buffer B
Initialize B = ∅ , and set current level l = E k
Observe current state s 0 , termination ﬂag dif d 0 then
Choose current level l
i ← S AMPLE N EXT L EVEL ( Λ train , S , C , c ) and E k ← l
Update level timestamp C i ← c
Observe initial state send if
Choose a 0 ∼ π θ ( ·| s 0 ) t =
Initialize episodic trajectory buffer τ = ∅ while t < T do
Observe ( s t , r t , d t )
B ← B ∪ ( s t − 1 , a t − 1 , s t , r t , d t , log π θ ( a ))
τ ← τ ∪ ( s t − 1 , a t − 1 , s t , r t , d t , log π θ ( a )) if d t then
Update level score S i ← score ( τ, π θ , ˜ S i ) and partial score ˜ S
i ←τ ← ∅
Update current level l ← S AMPLE N EXT L EVEL ( Λ train , S , C , c ) and E k ← l
Update level timestamp C i ← c end if
Choose a t +1 ∼ π θ ( ·| s t ) t ← t +end while if not d t then ˜ S i ← ( S ( τ ) , | τ | ) (cid:46) Track partial score and | τ | for time-averaged scores.
end if function S AMPLE N EXT L EVEL ( Λ train , S , C , c ) c
← c +
Sample replay decision d ∼ P D ( d ) if d = 0 and | Λ train \ Λ seen | > 0 then (cid:46)
Sample an unseen level, if any
Deﬁne new index i ← | S | +
Sample l i ∼ P new ( l | Λ train , Λ seen )
Add l i to Λ seen , add initial value 0 to S and C else (cid:46)
Sample replay
Sample
[Equation]
end if end function