PixL2R: Guiding Reinforcement Learning using Natural Language by Mapping Pixels to Rewards
Abstract
Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems.
To address this, several prior approaches have used natural language to guide the agent’s exploration.
However, these approaches typically operate on structured representations of the environment, and/or assume some structure in the natural language commands.
In this work, we propose a model that directly maps pixels to rewards, given a free-form natural language description of the task, which can then be used for policy training.
Our experiments on the Meta-World robot manipulation domain show that language-based rewards signiﬁcantly improve learning.
Further, we analyze the resulting framework using multiple ablation experiments to better understand the nature of these improvements.
1. Introduction
While reinforcement learning (RL) has been successfully used to solve many problems, designing good reward functions continues to remain a challenge, limiting its applicability to more complex problems.
To address this problem, several methods have been proposed that involve guiding an agent using natural language commands.
However, these techniques are still quite restrictive, often requiring object properties to be predeﬁned (MacGlashan et al., 2014;
Arumugam et al., 2017; Williams et al., 2017), assuming some structure in the natural language commands (Bahdanau et al., 2018), and/or requiring hand-designed features (Kuhlmann et al., 2004; Branavan et al., 2012b), which is challenging to scale.
In this work, we propose a framework that makes no such assumptions and directly learns to map pixels to rewards given a free-form natural
Correspondence to: language description of the task.
Our model is based on the framework proposed by Goyal et al.
(2019), which we describe next.
2. Prior Work: LEARN
Consider an extension of the standard Markov Decision
Process (MDP), deﬁned as M (cid:48) =
(cid:104) S, A, T, R, γ, L (cid:105) , where
L is an instruction describing the task using natural language, and the other quantities are as deﬁned in a standard
MDP.
Goyal et al.
(2019) proposed a framework for learning in this modiﬁed MDP (which they denote as MDP+L) consisting of the following two phases.
Phase 1 : A neural network – the LanguagE Action Reward
Network (LEARN) – is trained to predict whether a given trajectory and language are related or not.
This requires paired (cid:104) trajectory, language (cid:105) data in the environment.
As a preprocessing step, each trajectory is ﬁrst encoded into an action frequency vector , which is a vector of size | A | , with the i th component proportional to the number of times action i appears in the trajectory.
The neural network takes this action frequency vector and language as inputs to predict the relatedness between the trajectory and language, which is modelled as a binary classiﬁcation problem.
Phase 2 : Next, a policy is trained for a new task in the
MDP+L setting – the extrinsic reward from the environment is assumed to be sparse (i.e. 1 if the agent successfully completes the task, and 0 otherwise), and the agent additionally gets a language command describing the task.
At every step, an action frequency vector is created from the sequence of past actions and passed to the pretrained LEARN model along with the given command.
The LEARN model generates probabilities over classes RELATED and UNRELATED , which are used to generate intermediate rewards for reward shaping (Ng et al., 1999).
A signiﬁcant limitation of this work is that the relatedness model only uses the frequency with which each action is executed in the trajectory.
As such, information in the language instructions that requires knowledge of the state (e.g. how to interact with objects) is not helpful.
For instance, consider the domain shown in Figure 1, which is adapted from the recently released Meta-World benchmark (Yu et al., 2019).
The scene consists of a robot interacting with an object in the presence of zero or more other objects.
Different scenarios can be created in the domain by randomizing the set of objects in the scene and their positions.
Since linguistic descriptions of such tasks would typically be in terms of the object to be interacted with, whose position could change across different scenarios, learning a relatedness model between actions and language without taking into account the state (i.e. the image) will not generalize across scenarios.
Thus, we extend prior work to learn a relatedness model between sequences of states and language descriptions, and show that generating language-based scores from the resulting model improves the efﬁciency of policy training on unseen scenarios.
3. Approach
To apply the framework described in Section 2 to domains where using the state information is crucial to understanding language (e.g. the domain in Figure 1), we propose PixL2R, which takes in the pixel representations of the states and a natural language description of the task, and maps them to rewards.
The framework consists of a supervised learning phase (Section 3.1) and a policy training phase (Section 3.2) as in prior work, but with modiﬁcations as described below.
3.1. PixL2R: Pixels and Language to Reward
First, a relatedness model between a trajectory and a language is learned given paired data.
Our model is based on that proposed by Goyal et al.
(2019); however, instead of representing a trajectory using an action frequency vector before feeding into the neural network, we feed the sequence of frames in the trajectory directly into the neural network.
3.1.1. N ETWORK A RCHITECTURE
Using sequence of frames instead of an action frequency vector requires addressing perceptual aliasing and occlusion.
Thus, our network architecture is designed to take multiple views as inputs.
Speciﬁcally, we use three different viewpoints.
An independent CNN is used for encoding the sequence of frames from each viewpoint to generate a ﬁxed-size representation for each frame.
These sequence of vectors are concatenated across the views to generate a single sequence of ﬁxed-size vectors, which is then passed through a twolayer LSTM to get an encoding of the entire trajectory.
The language description is converted to a one-hot representation, and passed through an embedding layer, followed by a two-layer LSTM.
The outputs of the LSTMs encoding the trajectory and the language are then concatenated, and passed through a sequence of fully-connected layers to generate a relatedness score.
See supplementary material (Section A) for the viewpoints used, and a diagram of the neural network.
3.1.2. D ATA A UGMENTATION
Frame dropping.
Given an input trajectory, each frame is independently selected with a probability 0.1, and dropped with a probability 0.9.
The resulting sequence of frames is passed through the network.
This makes the training faster by reducing the input size, as well as making the network robust to minor variations in trajectories.
During policy training, the trajectories are uniformly subsampled to keep 1 frame in every 10.
Incomplete trajectories.
Since during policy training, the model will have to make predictions for incomplete trajectories, we use incomplete trajectories during supervised training as well.
To do this, given a trajectory of length L , we sample l ∼ Uniform { 1 , . . .
, L } , and use the ﬁrst l frames of the trajectory.
3.1.3. T RAINING O BJECTIVES
Classiﬁcation.
First, we trained the neural network using binary classiﬁcation, as in the original work.
The ﬁnal output of the network is a two-dimensional vector, corre- sponding to the logits for the two classes – RELATED and
UNRELATED .
Since partial trajectories might be difﬁcult to classify as related or unrelated, we experiment with an alternate regression-based setting, described below.
Regression.
In this setting, the model predicts a single relatedness score between the given trajectory and language, which is mapped to [ − 1 , 1] using the tanh() function.
The ground truth score is deﬁned as s · lL , where s = 1 for positive and s = − 1 for negative examples, l is the length of the incomplete trajectory and L is the length of the complete trajectory as described above.
Thus, given a description, a complete related trajectory has a ground truth score of 1 , while a complete unrelated trajectory has a score of − 1 .
Shorter trajectories smoothly interpolate between these values, with very small trajectories having a score close to 0 .
The network is trained to minimize the mean squared error.
3.2. Policy Training Phase
Having learned a PixL2R model as described above, the relatedness scores from the model can be used to generate language-based intermediate rewards during policy training on new scenarios.
During policy training, the agent receives a natural language description of the goal, in addition to the reward from the environment.
The PixL2R model is used to score trajectories executed by the agent against the given natural language description, to generate intermediate rewards.
We used potential-based shaping rewards (Ng et al., 1999), which are of the form F ( s t )
= γ · φ ( s t ) − φ ( s t − 1 ) , where s t is the state at timestep t and φ : S → R is a potential function.
In our case, s t is the sequence of states encountered by the agent up to timestep t in the current episode.
Ng et al.
and Grzes (2017) show that potential-based shaping rewards do not change the optimal policy, that is, the optimal policies under the original reward function R and the new reward function R + F are identical.
For the classiﬁcation setting, we used the potential function φ ( s t ) =
p R ( s t ) − p U ( s t ) , as deﬁned by Goyal et al.
Here, p R and p U are the probabilities assigned to the classes
RELATED and UNRELATED respectively.
For the regression setting, the relatedness score predicted by the model is directly used as the potential for the state.
Note that for both the settings, the potential of any state lies in [ − 1 , 1] .
4. Experiments 4.1. Dataset
We use Meta-World (Yu et al., 2019), a recently proposed benchmark for meta-reinforcement learning, which consists of a simulated Sawyer robot and everyday objects such as a faucet, windows, coffee machine, etc.
Tasks in this domain involve the robot interacting with these objects, such as turning the faucet clockwise, opening the window, pressing the button on the coffee machine, etc.
Completing these tasks requires learning a policy for continuous control in a 4-dimensional space (3 dimensions for the end-effector position, and the fourth dimension for the force on the gripper).
While the original task suite consists of only one object in every task, we create new environments which contain one or more objects in the scene, and the robot needs to interact with a pre-selected object amongst those.
In a sparse reward setting, the agent is given a non-zero reward only on successfully interacting with the pre-selected object.
In the absence of any other learning signal, the agent might have to learn to approach and interact with multiple objects in the scene in order to ﬁgure out the correct object.
Using natural language to describe the task in addition to the sparse reward helps alleviate this issue.
Different scenarios were created by randomizing the object to be interacted with, the distractor objects, and their positions.
Amazon Mechanical Turk was then used to obtain free-form natural language descriptions for these tasks.
The details of the process, as well as some example descriptions can be found in the supplementary material (Section C).
4.2. Policy Training with Language-based Rewards
To empirically evaluate the effectiveness of PixL2R, the following setup was used.
We had a total of 16 test scenarios, with 3 descriptions for each.
More details of the training/validation/test splits can be found in the supplementary material (Section B).
Each policy training was run for 500,000 timesteps using the PPO algorithm, and the number of successful completions of the task were recorded.
The maximum episode length was restricted to 500 timesteps.
The robot’s end-effector was set to a random position within a predeﬁned region at the beginning of each episode.
First, 15 policies were trained using sparse rewards ( Sparse ) on each test scenario with different seeds.
Next, policies were trained with language-based rewards using the regression setting, in addition to the sparse rewards ( Sparse+RGR ).
For each scenario, 5 policies were trained with different seeds for each of the 3 test descriptions, resulting in a total of 15 policy training runs per scenario.
A comparison of policy training curves for Sparse and
Sparse+RGR rewards is shown in Figure 2 (left).
Each curve is obtained by averaging over all runs (16 scenarios × 15 runs per scenario) for that reward type.
The results verify that language-based rewards result in higher performance on average than sparse ones.
Next, language-based rewards were used in addition to handdesigned rewards using a similar methodology, and the corresponding learning curves for Dense and Dense+RGR are shown in Figure 2 (right).
Interestingly, we ﬁnd that using language-based rewards in conjunction with handdesigned rewards result in an improvement even over handdesigned rewards.
Further, the improvements were found to be statistically signiﬁcant, both in sparse and dense reward settings, suggesting that the proposed approach can be used to make policy training more efﬁcient in both sparse and dense reward settings.
For the dense reward setting, policy training with languagebased rewards using the classiﬁcation setting ( Dense+CLS ) was also performed.
Although it results in an improvement in the mean successful episodes over Dense rewards, the improvement was not statistically signiﬁcant, suggesting that the proposed regression setting helps learning more effectively from partial trajectories.
More details for the results on the classiﬁcation setting, as well as other additional experiments used to analyze the supervised learning phase can be found in the supplementary material (Section C).
5. Related Work
A number of prior approaches have been proposed to use language to guide a learning agent.
Some approaches involve mapping natural language instructions directly to an action sequence to be executed (Tellex et al., 2011; Sung et al., 2018).
Our approach is different from these approaches in that we use language to generate auxiliary rewards, that can then be used to learn a policy using standard RL, allowing the agent to learn in more complex settings where ofﬂine learning is insufﬁcient.
Other prior approaches map natural language to a reward function (MacGlashan et al., 2014;
Arumugam et al., 2017;
Williams et al., 2017).
All these approaches assume a speciﬁc structure of the reward functions, while our approach does not make any such assumptions.
A number of approaches require designing state- and/or linguistic features to guide the learning agent (Kuhlmann et al., 2004; Branavan et al., 2012b; Kaplan et al., 2017;
Waytowich et al., 2019), whereas we propose to learn the association between language and trajectories from a small set of human-provided descriptions.
Some approaches learn to ground language while interacting with the environment (Branavan et al., 2012a; Misra et al., 2018; Bahdanau et al., 2018).
Our approach involves a separate supervised learning phase to ground language, which does not require interacting with the environment.
Fu et al.
(2019) learn a language-conditioned reward function, but require knowledge of environment dynamics to compute the optimal policy during training.
Narasimhan et al.
(2015) use natural language to transfer dynamics across environments.
Blukis et al.
(2019) generate a state visitation distribution given a natural language instruction, which is then used to generate rewards for policy training.
6. Conclusion
We proposed an approach for mapping pixels to rewards, conditioned on a free-form natural language description of the task.
Given paired (cid:104) trajectory, language (cid:105) data, ﬁrst, a relatedness model – PixL2R – is learned between a sequence of states and a natural language description using supervised learning.
This model is then used to generate intermediate rewards for policy training, for a task with natural language description.
Our experiments on a simulated robot manipulation domain show that the proposed approach can signiﬁcantly speed up policy learning, both in sparse and dense reward settings.
A. Neural Network architecture
Figure 3 shows the viewpoints used in our model.
Figure 4 shows a diagram of the neural network architecture described in Section 3.1.1.
B. Dataset Details
First, 13 tasks were selected from the Meta-World task suite.
This gave us a total of 9 objects to interact with (for 4 objects, multiple tasks can be deﬁned, e.g. turning a faucet clockwise or counter-clockwise).
We then created 100 scenarios for each task as follows:
In each scenario, the task-relevant object is placed at a random location on the table.
Then, a new random location is sampled, and one of the remaining objects is placed at this position.
This process is repeated until the new random location is close to an already placed object.
This results in 1300 scenarios in total, with a variable number of objects in each scenario.
A policy was trained for each of these scenarios independently using PPO (Schulman et al., 2017), which was then used to generate one video of the robot completing the task in the scenario.
For this purpose, we used the dense rewards deﬁned in the original Meta-World benchmark for various tasks.
The median length of trajectories across all generated videos is 131 frames.
Note that our algorithm does not need the policies used to generate the videos, so they could also be collected using human demonstrations.
To collect English descriptions from videos of the robot performing the tasks, Amazon Mechanical Turk (AMT) was used.
Since the models of the objects in the environment are coarse, it is usually non-trivial to recognize the real-world objects they represent from the models alone.
To guide the
AMT workers to use the names of real-world objects the models represent, we showed a table of the models with prototypical images of real-world objects that closely match the models (shown in Figure 5).
This enabled us to get descriptions that use the real-world object names, without priming the workers with speciﬁc words.
[Table]
[Figure]
We obtained 520 descriptions in total, for an average ofdescriptions per task.
Some example descriptions collected are shown in Table 1.
C. Details of the Experiments C.1. Experimental Setup
For each of the 13 tasks (see Section B), the 100 scenarios were randomly split into 80 training, 17 validation andtest scenarios.
For each of the test scenarios, ﬁrst, policy training was run with 15 random seeds, both in the sparse reward setting ( Sparse ; 1 if the agent reaches the goal, and 0 other- wise) and the hand-designed dense reward setting ( Dense ; deﬁned in the original Meta-World benchmark).
Then, a
Kruskal-Wallis test was used for each scenario to identify scenarios where there was a statistical signiﬁcant difference between the number of successful episodes with sparse rewards and with dense rewards, and the mean successful episodes with dense rewards was higher than the mean successful episodes with sparse rewards.
All subsequent comparisons were done on the 16 (out of 39) scenarios for which this was true.
Intuitively, these 16 tasks are too difﬁcult to learn from sparse rewards, while they can be learned using dense rewards.
Therefore, language-based dense rewards should be useful on these tasks.
The remaining tasks are presumably either too simple that they can be learned with sparse rewards alone, or are too difﬁcult to learn within 500,000 timesteps even with hand-designed dense rewards.
C.2. Word-level Analysis
In order to understand how the supervised learning phase is using different words in the description, the supervised model was used to make predictions on the test set, and the gradient of the loss was computed with respect to the continuous representation of the words in the descriptions
[Table]
pull down the lever on the toaster 0.
turn on the faucet 0.
slide the green lever to the left 0.
open the window 0.
(i.e. after the embedding layer).
The mean of the absolute values of these gradients is then a measure of how much the prediction is affected by the corresponding word.
The values are reported in Table 2, which were scaled so that the maximum value for any description is 1.
First, we observe that for all the descriptions, the words describing the main object have a very high average gradient magnitude – green and button in description 1, red and block in description 2, lever and toaster in description 3, faucet in description 4, green and lever in description 5, and window in description 6.
Several verbs also have a high average gradient magnitude – turn on in description 4 and open in window.
Verbs in other descriptions do not have a high gradient magnitude because for those descriptions, the object affords only one possible interaction, thus making the verb less discriminatory.
For the objects faucet and window , there are two possible actions each ( turning the faucet on or off and opening or closing the window ); thus the verb also carries useful information for these objects.
This analysis suggests that the model learns to identify the most salient words in the description that are useful to predict the relatedness between a trajectory and language.
C.3. Ablations
Having established that policy training works better with the language-based rewards, we ran some ablation experiments as described below.
All the ablation experiments were performed with language-based rewards added to dense rewards, since most applications of RL currently use dense hand-designed rewards (which could be suboptimal for complex tasks), and it would be informative to learn which design decisions are most important to get an improvement by using language-based rewards in such settings.
• LastFrame : Instead of using the sequence of frames in the trajectory, only the last frame of the trajectory was used, both for training the PixL2R model, as well as for policy training.
• MeanpoolLang :
The LSTM used to encode the language was replaced with the mean-pooling operation.
• MeanpoolTraj :
The LSTM used to encode the sequence of encoded frames was replaced with the meanpooling operation.
• SingleView :
Instead of using 3 viewpoints for the trajectory, only one viewpoint was used.
• Dense+CLS :
Instead of the regression loss, classiﬁcation loss was used, as proposed in (Goyal et al., 2019).
For each ablation, the same setup was used as for
Dense+RGR – training the PixL2R model with 8 random sets of values of hyperparameters, and choosing the model with the best validation accuracy.
This model is used to generate rewards for policy training, for each of the 16 scenarios with 5 random seeds for all the 3 descriptions.
The mean successful episodes across all runs are reported in Table 3.
Further, the p-values for Wilcoxon tests between each ablation and the Dense rewards is reported, from
, we
[Table]
which we can make the following observations:
• Using only the last frame ( LastFrame ), or using mean-pooling instead of an LSTM to encode the language ( MeanpoolLang ) does not substantially affect the performance of the model.
In both these cases, the resulting model is still statistically signiﬁcantly better than Dense rewards.
Both of these results agree with intuition, since the last frame can be used to predict the progress in the task, and since the linguistic descriptions are not particularly complex in the given domain, simply looking at which words are present or absent is often sufﬁcient to identify the task without using the ordering information between the words.
• Using mean-pooling instead of an LSTM to encode the sequence of frames ( MeanpoolTraj ) drastically reduces the number of successful episodes, and the resulting model is no longer statistically signiﬁcantly better than Dense .
Again, this agrees with intuition, since it is not possible to infer the direction of movement of the robot from an unordered set of frames.
• Using a single view instead of multiple views ( SingleView ) results in a decrease in the number of successful episodes, and the resulting model is no longer statisticially signiﬁcantly better than Dense .
As mentioned earlier, using frames to represent trajectories (instead of actions as in prior work) requires addressing challenges such as perceptual aliasing and occlusion, and these ablation results suggest that using multiple viewpoints alleviates these issues.
• Using classiﬁcation loss instead of regression ( Dense+CLS ) also leads to a drop in performance, again making the resulting model no longer statistically signiﬁcantly better than Dense .
This is consistent with our initial observation, as described in Section 3.1.3, wherein, the learning problem becomes more difﬁcult due to partial trajectories when the classiﬁcation loss is used.