M ULTI -E NVIRONMENT
P RETRAINING E NABLES T RANSFER TO A CTION L IMITED D ATASETS
Anonymous authors
Paper under double-blind review
A BSTRACT
Using massive datasets to train large-scale models has emerged as a dominant approach for broad generalization in natural language and vision applications.
In reinforcement learning, however, a key challenge is that available data of sequential decision making is often not annotated with actions - for example, videos of gameplay are much more available than sequences of frames paired with the logged game controls.
We propose to circumvent this challenge by combining large but sparsely-annotated datasets from a target environment of interest with fullyannotated datasets from various other source environments.
Our method, Action
Limited PreTraining (ALPT), leverages the generalization capabilities of inverse dynamics modelling (IDM) to label missing action data in the target environment.
We show that utilizing even one additional environment dataset of labelled data during IDM pretraining gives rise to substantial improvements in generating action labels for unannotated sequences.
We evaluate our method on benchmark game-playing environments and show that we can signiﬁcantly improve game performance and generalization capability compared to other approaches, even when using annotated datasets equivalent to only 12 minutes of gameplay.
I NTRODUCTION
The training of large-scale models on large and diverse data has become a standard approach in natural language and computer vision applications (Devlin et al., 2019; Brown et al., 2020; Mahajan et al., 2018;
Zhai et al., 2021).
Recently, a number of works have shown that a similar approach can be applied to tasks more often tackled by reinforcement learning (RL), such as robotics and game-playing.
For example, Reed et al.
(2022) suggest combining large datasets of expert behavior from a variety of RL domains in order to train a single generalist agent, while Lee et al.
(2022) demonstrate a similar result but using non-expert (ofﬂine RL) data from a suite of Atari gameplaying environments and using a decision transformer (DT) sequence modeling objective (Chen et al., 2021b).
Applying large-scale training necessarily relies on the ability to gather sufﬁciently large and diverse datasets.
For RL domains, this can be a challenge, as the most easily available data – for example, videos of a human playing a video game or a human completing a predeﬁned task – often does not contain labelled actions, i.e., game controls or robot joint controls.
We call such datasets action limited , because little or none of the dataset is annotated with action information.
Transferring the success of approaches like DT to such tasks is therefore bottlenecked by the ability to acquire action labels, which can be expensive and time-consuming (Zolna et al., 2020).
Some recent works have explored approaches to mitigate the issue of action limited datasets.
For example, Video PreTraining (VPT) (Baker et al., 2022) proposes gathering a small amount ( 2 k hours of video) of labeled data manually which is used to train an inverse dynamics model (IDM)
(Nguyen-Tuong et al., 2008); the IDM is then used to provide action labels on a much larger videoonly dataset ( 70 k hours).
This method is shown to achieve human level performance in Minecraft.
While VPT shows promising results, it still requires over 2 k hours of manually-labelled data; thus, a similar amount of expensive labelling is potentially necessary to extend VPT to other environments.
In this paper, we propose an orthogonal but related approach to VPT: leveraging a large set of labeled data from various source domains to learn an agent policy on a limited action dataset of a target evaluation environment.
To tackle this setting, we propose Action Limited Pretraining (ALPT), which relies on the hypothesis that shared structures between environments can be exploited by noncausal (i.e., bidirectional) transformer IDMs. This allows us to look at both past and future frames to infer actions.
In many experimental settings, the dynamics are far simpler than multi-faceted human behavior in the same setting.
It has been suggested that IDMs are therefore more data efﬁcient as well as empirically shown (Baker et al., 2022).
ALPT thus uses the multi-environment source datasets as pretraining for an IDM, which is then ﬁnetuned on the action-limited data of the target environment in order to provide labels for the unlabelled target data, which is then used for training a DT agent.
Through various experiments and ablations, we demonstrate that leveraging the generalization capabilities of IDMs is critical to the success of ALPT, as opposed to, for example, pretraining the DT model alone on the multi-environment datasets or training the IDM only on the target environment.
On a benchmark game-playing environment, we show that ALPT yields as much as 5 x improvement in performance, with as little as 10 k labelled samples required (i.e., 0 .
01% of the original labels), derived from only 12 minutes of labelled game play (Ye et al., 2021).
We show that these beneﬁts even hold when the source and target environments use distinct action spaces; i.e., the environments share similar states but no common actions, further demonstrating the power of IDM pretraining.
While ALPT is, algorithmically, a straightforward application of existing ofﬂine RL approaches, our results provide a new perspective on large-scale training for RL.
Namely, our results suggest that the most efﬁcient path to large-scale RL methods may be via generalist inverse dynamics modelling paired with specialized agent ﬁnetuning, instead of generalist agent training alone.
R ELATED W ORK
In this section, we brieﬂy review relevant works in multi -task RL, meta-learning for RL, semi - supervised learning
and transfer learning
Multi-Task RL.
It is commonly assumed that similar tasks share similar structure and properties (Caruana, 1997; Ruder, 2017; Zhang et al., 2014; Radford et al., 2019a).
Many multi-task RL works leverage this assumption by learning a shared low-dimensional representation across all tasks (Calandriello et al., 2014; Borsa et al., 2016; D’Eramo et al., 2020).
These methods have also been extended to tasks where the action space does not align completely (Br¨am et al., 2020).
Other methods assume a universal dynamics model when the reward structure is shared but dynamics are not (Zhang et al., 2021a).
Multi-task RL has generally relied on a task identiﬁer (ID) to provide contextual information, but recent methods have explored using additional side information available in the task meta-data to establish a richer context (Sodhani et al., 2021).
ALPT can be seen as multi-task
RL, given that we train both the sequence model and IDM using multiple different environments, but we do not explicitly model context information or have access to task IDs.
Meta RL.
Meta-learning is a set of approaches for learning to learn which leverages a set of metatraining tasks (Schmidhuber, 1987; Bengio et al., 1991), from which an agent can learn either parts of the learning algorithms (eg how to tune the learning rate) or the entire algorithm (Lacombe et al., 2021;
Kalousis, 2002).
In this setting, meta-learning can be used to learn policies (Duan et al., 2017;
Finn et al., 2017) or dynamics models (Clavera et al., 2019).
A distribution of tasks is assumed to be available for sampling, in order to provide additional contextual information to the policy.
One such method models contextual information as probabilistic context variables which condition the policy (Rakelly et al., 2019).
This method has been shown to learn from only a handful of trajectories.
Meta-training can be used to learn policies ofﬂine, while using online interaction to correct for distribution shift, without requiring any rewards in the online data (Pong et al., 2022).
These methods are commonly used to train on a source set of tasks, like ALPT, but usually require task labels.
Meta-training tasks need to be hand-selected, and the results are highly dependent on the quality of that process.
Semi-supervised learning.
Semi-supervised learning uses both labelled and unlabelled data to improve supervised learning performance (Zhu et al., 2009).
It is especially useful when a limited amount of labelles data is given and additional labels are difﬁcult to acquire, unlabelled data is plentiful.
Early methods of this type infer unknown labels using a classiﬁer trained on the labeled data (Zhu & Ghahramani, 2002).
Other methods rely on additional structural side information to regularize supervised objectives (Szummer & Jaakkola, 2001), such as the time scale of a Markov random walk over a representation of the data.
Many methods, especially those using deep learning, combine supervised and unsupervised learning objectives (Rasmus et al., 2015).
More recent methods use generative models and approximate Bayesian inference to ﬁll in missing labels (Kingma et al., 2014).
The problem of semi-supervised learning is especially relevant in RL, where large datasets of experience containing action descriptions or rewards may hard to acquire, eg. through manual annotation of videos or running robotic experiments.
By using an inverse dynamics model,
ALPT applies semi-supervised learning to label actions in a large dataset of experience frames, given only limited labeled action data.
Transfer Learning and Zero-shot RL.
Policies learned by RL in one domain can have limited capability to generalize to new settings (Oh et al., 2016).
The most difﬁcult problem is zero-shot
RL, where the agent must generalize at evaluation time to a new environment that was not seen in training, without acquiring any new data.
Transfer learning (Taylor & Stone, 2009) tackles a subset of generalization problems where the agent can access interactions from a related environment or task during training.
This prior experience in other environments is leveraged to improve learning in novel environments.
Transfer learning has been applied across both environments (Mordatch et al., 2016;
Tzeng et al.) and tasks (Rusu et al., 2016; Parisotto et al., 2016).
It has also been examined in hard exploration games, using imitation learning from human-generated video data (Aytar et al., 2018).
ALPT can be seen as tackling the transfer learning problem, with limited action data from the target environment and providing pseudo-labels for actions.
Notably, we consider the underexplored scenario in which the action space is not completely shared between the training and test environments. Ofﬂine RL.
Ofﬂine RL describes a setting that during learning, the agent has access to only a ﬁxed dataset of experience.
When function approximation becomes necessary in environments with large, complex state spaces, online RL algorithms are extremely sensitive to the dataset distribution.
It has been shown that even when the logged data is collected with a single behavior policy, standard online algorithms fail (Fujimoto et al., 2019).
This has been hypothesized to be due erroneous generalization of the state action function.
One such class of methods applies random ensembles of Q-value function targets (Agarwal et al., 2020a) to alleviate this source of error. Other works suggest regularizing the agent policy to the behavior policy (Zhang et al., 2021b). In ofﬂine RL, errors arise when needed to bootstrap the value function and algorithms must apply strong regularizations on both learned policy and value function to achieve stable performance (Wu et al., 2020; Kumar et al., 2019;
Zhang et al., 2021b; Nachum et al., 2019).
Decision transformer has been suggested as an ofﬂine RL strategy that has been successful in learning generalizable agents from a wide variety of logged data in multiple unrelated environments (Reed et al., 2022).
B ACKGROUND
In this section, we review the standard ofﬂine RL setting and the use of decision transformers (DT) as a sequence modelling objective for ofﬂine RL.
We then deﬁne the setting of multi-environment ofﬂine RL with action-limited data, which is our focus.
3. O FFLINE R EINFORCEMENT L EARNING
We consider an agent acting within a Markov decision process (MDP) deﬁned by h S , A , P , R i , where S is the set of states, A is the set of actions, P : S ⇥ A !
Dist ( S ) is the transition probability kernel and R : S ⇥ A !
[0 , 1] is the scalar reward function.
In ofﬂine RL, the agent is given a dataset of episodes, i.e., sequences of states, actions, and rewards collected by unknown policies interacting with the environment:
[Equation]
The objective is typically to use this dataset in order to learn a conditional action distribution,
P ✓ ( a t | s  t , a <t , r <t ) , that maximizes the expectation of the total return, G t = P k [-≥ 
-] 0 r t
+ k when used to interact with the environment from which the training episodes were generated.
3. O FFLINE RL AS S EQUENCE M ODELING
Decision transformer (DT) (Chen et al., 2021a) is an approach to ofﬂine RL which formulates this problem as sequence modeling, and then uses transformer-based architectures to solve it.
For this purpose, the episodes in the ofﬂine dataset are augmented with the returns associated with each step:
[Equation]
This sequence is tokenized and passed to a causal transformer P ✓ , which predicts both returns and actions using a cross-entropy loss.
Thus, the learning objective for ✓ is:
[Equation]
During inference, at each timestep t , after observing s t , DT uses the predicted return distribution P ✓ ( G t | s  t , a <t , r <t ) to choose an optimistic estimate ˆ G t of return, before using
P ✓ ( a t | s  t , a <t , r <t , ˆ G t ) to select an action ˆ a t (see Lee et al.
(2022) for details).
3. M ULTI -E NVIRONMENT AND A CTION L IMITED D ATASETS
Our goal is to use pretraining on a set of environments where labelled data is plentiful, in order to do well on a target environment where only limited action-labelled data is available.
Therefore, the ofﬂine RL setting we consider includes multiple environments and action-limited datasets, as we detail below.
We consider a set of n source environments, deﬁned by a set of MDPs: E = {M 1 , . . .
, M n } , and a single target environment M ? .
For each source environment M d , we have an ofﬂine dataset of episodes generated from M d , denoted by D d = { ⌧ := h . . .
, s t , a t , r t , . . .
i } , fully labelled with actions.
The agent is trained using the ofﬂine dataset of episodes: ( S nd =1 D d ) . For the target environment, the agent has access to a small labelled dataset from M ? , denoted as D + ?
= { ⌧ := h . . .
, s t , a t , r t , . . .
i } , and a large dataset without action labels, D  ?
= { ⌧ := h . . .
, s t , r t , . . .
i } .
A CTION L IMITED P RETRAINING (ALPT)
We now describe our proposed approach to ofﬂine RL in multi-environment and action limited settings.
ALPT relies upon an inverse dynamics model (IDM) which uses the combined labelled data in order to learn a representation that generalizes well to the limited action data from the target environment.
The predicted labels of the IDM on the unlabelled portion of the target environment dataset are then used for training a sequence model parameterized as a decision transformer (DT).
We elaborate on this procedure below and summarize the full algorithm in Table 1. [Table] Evaluation [Table]
interact
[Table] target [Table] M
[Table] 4. I NVERSE D YNAMICS M ODELING Our inverse dynamics model (IDM) is a bidirectional transformer trained to predict actions from an action-unlabelled sub-trajectory of an episode.
The training objective for learning an IDM P  is
[Equation]
where k is the length of training sub-trajectories.
In our experiments, we parameterize P  using the GPT-2 transformer architecture (Radford et al., 2019b) and use k = 5 .
We obtained k through experimental tuning.
4. M ULTI -E NVIRONMENT P RETRAINING AND F INETUNING
ALPT is composed of a two-stage pretraining and ﬁnetuning process.
During pretraining , we use the combined labelled datasets for all source environments combined with the labelled portion of the target environment dataset: ( S nd =1 D d ) [ D + ? , to train the IDM P  .
Concurrently, we also train the DT P ✓ on the combined labelled and unlabelled datasets for all source environments combined with the target environment datasets, by using the IDM to provide action labels on the unlabelled portion D  ? . The DT training dataset is therefore: ( [Equation]
During ﬁnetuning , we simultaneously train both the IDM and DT exclusively on the target environment dataset.
We train the IDM on the labelled portion D + ? .
We train DT on the full action limited dataset D + ? [ D  ? by using the IDM to provide action labels on the unlabelled portion D  ? .
Finally, during evaluation we use the trained DT agent to select actions in the target environment
M ? , following the same protocol described in Section 3.2.
E XPERIMENTS
We evaluate ALPT on a multi-game Atari setup similar to Lee et al.
Our ﬁndings are threefold: (1) ALPT, when pretrained on multiple source games, demonstrates signiﬁcant beneﬁts on the target game with limited action labels; (2) ALPT maintains its signiﬁcant beneﬁts even when pretrained on just a single source game with a disjoint action space, (3) we demonstrate similar beneﬁts on maze navigation tasks.
5. E XPERIMENTAL P ROCEDURE
Architecture and Training.
Our architecture and training protocol follow the multi-game Atari setting outlined in Lee et al.
Speciﬁcally, we use the 40M-sized model from Lee et al.
but reduce the model’s hidden size from 768 to 512, as we only pretrain on at most a subset of Atari games.
We used 6 transformer layers, which is the same as 40M-sized model of 8 heads each, which is the same as the 10M-sized model.
The rest of the architecture and training hyperparameters [Table] ALPT
[Table] remain unchanged for experiments on Atari. For the Maze navigation experiments, we modify the original hyperparameters to use a batch size of 256 and a weight decay of 5 ⇥ 10  5 .
During pretraining, we train the DT and IDM for 1 M frames.
Datasets As in Lee et al.
(2022), we use the standard ofﬂine RL Atari datasets from RL Unplugged (Gulcehre et al., 2020).
Each game’s dataset consists of 100M environment steps of training a DQN agent (Agarwal et al., 2020b).
For the source games, we use this dataset in its entirety.
For the target game, we derive an action-limited dataset by keeping the action labels for randomly sampled sequences consisting of a total 10k transitions (0.01% of the original dataset, equivalent to 12 minutes of gameplay) and removing action labels in the remainder of the dataset.
For the maze navigation experiments, we generate the data ourselves.
The ofﬂine datasets for each maze conﬁguration contain 500 trajectories with a length of 500 steps or until the goal state is reached.
They are generated using an optimal policy for each maze, with an ✏ -greedy exploration rate of 0 .
5 to increase data diversity.
5. B ASELINE M ETHODS
We detail the methods that we compare ALPT to below.
Single-game variants.
To evaluate the beneﬁt of multi- versus single-environment training, we assess the performance of training either DT alone or DT and IDM simultaneously on the target game.
When training DT alone ( DT1 ), we train it only on the 10 k subset of data that is labelled, while when training DT and IDM simultaneously ( DT1-IDM ) we use the IDM to provide action labels on the unlabelled portion of the data.
Multi-game DT variants.
To assess the need for IDM versus training on DT alone, we evaluate a multi-game baseline ( DT5 ) composed of DT alone.
For this baseline, we pretrain DT on all labelled datasets combined from both the source and target environments before ﬁnetuning the DT model on the 10 k labelled portion of the target game.
Return prediction DT variants.
As an alternative way for DT to leverage the unlabelled portion of the target environment dataset, we evaluate a baseline ( DT5-RET ) that uses the unlabelled portion for training its return prediction.
The model still undergoes a pretraining and ﬁnetuning stage, ﬁrst pretraining on all available data and then ﬁnetuning only on data from the target game.
We give a summary of the baseline methods as well as ALPT in Table 2.
5. H OW DOES ALPT PERFORM COMPARED TO THE BASELINES ?
We focus our ﬁrst set of multi-game pretraining experiments on 5 Atari games: { Asterix, Breakout,
SpaceInvaders, Freeway, Seaquest } .
This subset of games is selected due to having a similar shared game structure and access to high-quality and diverse pretraining data.
We evaluate each choice of target game in this setting, i.e., for each game we evaluate using it as the target game while the remaining 4 games comprise the source environments.
We compare our pretraining regime (ALPT) with the single-game variant and standard DT baselines in Figure 2.
We see that pretraining ALPT on the source games results in substantial downstream performance improvements.
We show that there are relatively minimal performance improvements when pretraining on datasets that do not include any non-target environments (DT 1 -IDM).
Utilizing
ALPT results in improvements up to ⇡ 500% higher than the single-game training regime.
The performance difference is especially stark in Breakout and Seaquest .
Additionally, we that performance is not recovered under pretraining only the sequence model (DT 5 ) on the action rich environments, indicating that most generalization beneﬁts are occurring due to the IDM pretraining.
We also show that performance using DT1 and DT5-RET is poor, highlighting the need for explicit re-labelling to achieve good performance during sequence model ﬁnetuning.
5. I S DYNAMICS MODELLING IMPROVED UNDER MORE OR LESS SOURCE DATASETS ?
As an experimental ablation, we explore if having more or less source data to train our dynamics model results in better performance.
In this set of experiments, we expand or reduce the set of source environments during pretraining of both our DT and IDM.
Results are presented in Figure 3.
We ﬁnd that only a limited number of source environments is needed for downstream performance improvements.
We do not ﬁnd that performance is signiﬁcantly improved by increasing the environment diversity of the training dataset, highlighting the versatility of pretraining the IDM even given a single related dataset.
These results indicate both the practicality of ALPT and fast adaptation ability during ﬁnetuning.
5. C AN ALPT HELP WHEN SOURCE AND TARGET HAVE DISJOINT ACTION SPACES ?
The previous experiments have included at least one source game during pretraining whose the action space overlaps with that of the target environment.
The next set of experiments aims to explore pretraining on source environments where the action space ( A d ) is disjoint with the target environment action space ( A ⇤ ), that is, A d \ A ⇤ = ; .
To do so, we use Freeway as our single source environment dataset.
In Freeway , the action space consists of { Up, Down } .
In contrast, a game such as Breakout has an action space consisting of { Left, Right } .
Surprisingly, using Freeway as a source environment and Breakout as a target environment still yields signiﬁcant beneﬁts for ALPT.
We present this result in Figure 4, as well as a variety of other choices for the target environment, none of which share any actions with the source environment Freeway .
We hypothesize that performance improvements are not unexpected due to the already known broad generalization capabilities of DT architectures. It may also be the case that, despite the action spaces being disjoint, they still exhibit similar structure. For example, a top/down action space vs. a left/right action space are structurally opposite to each other in terms of movement, differing only in orientation. This similar structure is potentially learned and leveraged during ﬁnetuning
5. D O ALPT’ S BENEFITS PERSIST IN OTHER DOMAINS ?
We now demonstrate ALPT’s beneﬁts on an action-limited navigation task.
This corresponds to a scenario where we have densely annotated navigation maps for a set of source regions but only a sparsely annotated navigation map for a target region.
We would like to evaluate whether ALPT, pretrained on source regions with abundant action labels, can generalize to navigating in a target region (of a different layout) with limited action labels.
Maze Navigation Environments.
To answer the above question, we consider a gridworld
To collect the ofﬂine training datasets, we follow Yang et al.
; Zhang et al.
(2018) to algorithmically generate maze layouts with random internal walls that form blocked or tunneled obstacles as shown in Figure 5.
We start with blocked obstacles, and generate one source maze from which we collect 500 trajectories with full action labels.
We then use a different random seed to generate the target maze, from which we collect 500 trajectories with only 250 action labels ( 0 .
5% of the [Figure] full action labels).
We then train the IDM of ALPT-Blocked on both the source and target datasets, labeling the missing actions from the target game, and train DT all at the same time (no separate ﬁnetuning stage).
ALPT-Blocked and other baselines are evaluated in the target maze only.
Results.
Performance in the target maze environment with limited action labels is presented in
Figure 5 (b).
ALPT-Blocked trained on both source and target mazes allows us to solve the target task twice as fast compared to only training on the target maze without access to another source maze.
To further illustrate the beneﬁt of multi-environment training on more diverse data, we introduce the tunnelled maze, and train ALPT-Blocked+Tunnelled on 500 trajectories with full actions from a source blocked maze and a source tunneled maze, respectively, as well as 250 action samples of the target blocked maze.
Training on both tunnelled and blocked mazes enables greater dataset diversity, which further improves generalization, leading to even faster convergence on the target task.
These preliminary results on navigation suggest that multi-environment pretraining can beneﬁt a broad set of tasks.
C ONCLUSION
We explored the problem of learning agent policies from action limited datasets.
Inspired by the paradigm of large-scale, multi-environment training, we proposed ALPT, which pretrains an inverse dynamics model (IDM) on multiple environments to provide accurate action labels for a decision transformer (DT) agent on an action limited target environment dataset.
Our experiments and ablations highlight the importance of pretraining the IDM, as opposed to pretraining the DT agent alone.
Our results support the importance of generalist inverse dynamics models as an efﬁcient way to implement large-scale RL.
As more labelled data becomes available for training ofﬂine RL agents,
ALPT provides an efﬁcient way of bootstrapping performance on new tasks with action limited data.
6. L IMITATIONS
The largest limitation of ALPT is the assumption that we would have plentiful labelled data from related environments.
One interesting fact we uncover is that this data does not have to be based on the same action space as the desired target environment, indicating the versatility of ALPT and its ability to ingest diverse source environment training data.
We also caution that we have only evaluated ALPT so far on limited, self-contained video game tasks and simple navigation environments.
We hope that as more labelled data becomes available in RL domains, ALPT will have wider applicability, allowing RL agents to scale and bootstrap to new environments.
It would also be useful to investigate further how much labelled data from a limited set of source environments is required to be able to handle a much larger set of unlabelled datasets.