user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: I love your chain mail! Making knights smile in a fantasy game world

Paper content:
Dialogue research tends to distinguish between chit-chat and goal-oriented tasks.
While the former is arguably more naturalistic and has a wider use of language, the latter has clearer metrics and a more straightforward learning signal.
Humans effortlessly combine the two, and engage in chit-chat for example with the goal of exchanging information or eliciting a specific response.
Here, we bridge the divide between these two domains in the setting of a rich multi-player text-based fantasy environment where agents and humans engage in both actions and dialogue.
Specifically, we train a goal-oriented model with reinforcement learning via self-play against an imitation-learned chit-chat model with two new approaches: the policy either learns to pick a topic or learns to pick an utterance given the top-k utterances.
We show that both models outperform a strong inverse model baseline and can converse naturally with their dialogue partner in order to achieve goals.
In the literature on artificial dialogue agents, a distinction is often made between "goal-oriented" dialogue, where an agent is tasked with filling slots or otherwise obtaining or disseminating specified information from the user to help complete a task, and "chit-chat", where an agent should imitate human small talk.
Modeling goal-oriented dialogue can have advantages over chit-chat imitation as it gives clearer metrics of success and perhaps more meaningful learning signals; but goal-oriented dialogue data is often more specialized, covering only a narrow slice of natural language.
Current goal-oriented datasets study setting like booking restaurants or airline tickets, or obtaining weather information, as standalone tasks (Raux et al., 2005; Henderson et al., 2014; Bordes et al., 2017; El Asri et al., 2017; Budzianowski et al., 2018) .
Chit-chat agents, by contrast, might focus on coarse statistical regularities of dialogue data without accurately modeling the underlying "meaning"; but the data often covers a much wider space of natural language.
For example, Twitter or Reddit chitchat tasks (Li et al., 2016a; Yang et al., 2018; Mazaré et al., 2018 ) cover a huge spectrum of language and diverse topics.
Chit-chat and goal-oriented dialogue are not mutually exclusive: when humans engage in chit-chat, their aim is to exchange information, or to elicit specific responses from their partners.
Modeling such goals, however, is made difficult by the fact that it requires large amounts of world knowledge, and that goals in real life are implicit.
In this work, we study goal-oriented dialogue agents in the setting of a multi-player text-based fantasy environment (Urbanek et al., 2019) .
The environment is built on top of a game engine that grounds actions and reference objects, and thus codifies a body of world-knowledge.
Although the interactions between objects and characters are simulated, the choice and types of interactions, the text used to describe them, and the dialogues between characters, are "natural" and wide-ranging, having been collected from human crowdworkers.
We define the general task of, given a particular character in a particular scenario (location, set of objects and other characters to interact with) to conduct open-ended dialogue such that a given action is executed in the future by their dialogue partner.
The given action could be an emote action (smile, laugh, ponder, . . . ), or a game action (wear chain mail, drink mead, put glass on table, . . . ).
The richness of the environment means that there are a huge set of possible tasks and scenarios in which to achieve a wide range of actions.
Thus, this task is ideally suited for bridging the divide between goal-oriented and chit-chat dialogue, combining clearer metrics and learning signals on the one hand, with the richness and complexity of situated but open-domain natural language on the other.
Figure 1 : Example episode from the LIGHT dataset, consisting of an environment (location setting, characters with given personas, objects), utterances and game actions.
There are 10,777 such humanhuman gameplay episodes, and a rich world of 663 locations, 1755 characters and 3462 objects.
We train models to achieve these tasks using reinforcement learning (RL) and a type of self-play between two agents.
The first agent, which we call the environment agent, is trained with imitation learning on human-human interactions (game actions, utterances and emotes) and subsequently kept fixed.
The second agent, the RL agent, is trained to conduct dialogue given the goal, and the two agents interact within a given environment until the goal is either reached or a given number of turns has expired.
At that point, rewards are given, and the RL agent is updated.
We compare agents that have been trained to imitate human actions given a goal (an "inverse model") to two different RL approaches: optimizing actions with latent discrete variables (topics), or via rewarding actions sampled from the model (via the top-K outputs).
We show that both types of RL agent are able to learn effectively, outperforming the inverse model approach or a vanilla chit-chat imitation baseline, and can converse naturally with their dialogue partner to achieve goals.
We work in the LIGHT game environment (Urbanek et al., 2019) , which is a multi-user text-based game, involving many characters playing the game at once.
Characters (either played by humans or run by models) can speak to to each other via free text, send emote actions like applaud, nod or pout (22 emote types in total), and take actions to move to different locations and interact with objects (e.g. get cutlery, put cutlery in drawer, etc.), see Appendix A for a full list of game actions.
LIGHT at its core has a game engine which can formally be defined as a graph, where each location, object and character is a node, and they are connected by labeled edges representing relationships, for example contained-in, path-to or has-property.
Actions in the game result in changes in state of the graph.
To a player (agent) a local view of the graph can be seen and this is expressed in text, as are the game actions and changes of state.
This text then naturally interleaves with the dialogue utterances of the speakers as well to form a input context sequence from which a character can base their subsequent actions.
See Fig. 1 for an example.
To make the world and its textual descriptions, LIGHT consists of a large set of human-written game locations, characters, and objects, all based within a fantasy medieval setting.
Their names, descriptions and properties were crowd-sourced, yielding a total of 663 locations, 1755 characters, and 3462 objects.
They range from beaches with crabs and seaweed to crypts with archaeologists and coffins, yielding an extremely rich environment for agents to learn within.
An additional set of crowdworkers were then asked to play the role of characters (randomly picked from the set of 1755) within the created world as rendered by the game engine.
This involved them making utterances, game actions and emotes, while interacting with each other (in pairs).
The resulting gameplay data consists of 10,777 episodes with an average of 18.3 actions each (game actions, emotes and utterances) of rich human play.
These are split into train (8538), validation (500) and test (1739) portions, the latter being split into new episodes in existing settings (test seen, 1000) and completely new settings (test unseen, 739) .
This gameplay data can be used for training models using imitation learning, as well as for obtaining "common sense" knowledge about how the world works, i.e., what kinds of things certain characters say; what actions they use with certain objects; what they say and how they act in certain environments or while interacting with certain other characters.
The whole environment is thus intended as a proxy for learning about the world within a rich simulation, while avoiding the complexities and bandwidth of rendering (3D) computer graphics.
While players were not given specific goals, but instead asked to play the role convincingly of the character given, during play some of them effectively defined their own goals during the interactions, see Fig. 1 .
The tasks we consider in this work involve interaction between two agents in a given LIGHT scenario.
One of the agents, which we will call M env , together with the game engine, effectively functions as an environment for the other agent, which we will call M RL .
Because we will formulate our tasks as a reinforcement learning problem, we will also refer to M env as the "environment agent" and M RL as the "RL agent".
We assume that the environment agent is fixed; in this work it will be a model trained via behavioral cloning from human-human interaction data.
The RL agent must conduct open-ended dialogue such that a given goal action is executed in the future by the environment agent.
Our task is formally defined as follows.
The two agents M env and M RL are given their views of the scenario (D env and D RL respectively).
These consist of the setting name, scenario description, character names, and their own persona, all described as a sequence of text (see Fig 2) .
Note that each agent can only access their own persona but not the persona of the partner with whom they are conversing, but they do know the name of their partner.
Denote by t the time-step of the environment, U RL t and U env t the utterances of the agents M RL and M env respectively, and denote by A env t the environment actions by M env .
Hence the interaction sequence looks like
Note that there is an inversion from the usual reinforcement literature language, as the "actions" of the RL agent are its utterances U RL t ; the actions A env t of the environment agent should be considered as internal mechanics of the environment.
The agent M RL is additionally given a goal g to achieve, which consists of an action which must be executed by the other agent.
That is, the objective of M RL is for M env to take the action g. An episode ends when A env t == g or when n becomes larger than a set number of turns.
The RL agent only speaks, but does not perform game or emote actions.
This was chosen for simplicity, but also to guarantee that the RL agent cannot help force the goal to be reached by performing actions itself -it has to pick the appropriate utterances U RL such that M env eventually takes the action g.
Goals We experiment separately with two different types of goals: game actions and emote actions.
We use the same train, valid, test (seen and unseen) split of the original human-human LIGHT episodes, assign roles M RL and M env randomly, and randomly pick an action by M env that occurs in the episode as the goal.
We can then present the corresponding setting to our agents in order to form a new interaction, but within the same scenario and with a goal that was naturally desirable and achievable within that setting.
Observations The state observation O t = (D RL , S t−1 , g) at time t given to an RL model consists of the RL agent's setting description (D RL ), the utterance and action history up to that time step (S t−1 ), and the agent's goal (g).
Our RL agent models consume O t as a flattened sequence of tokens, and return a dialogue utterance U RL t .
Each structured component is represented in the flattened sequenced separated by a special token denoting the types, e.g. names, settings, etc., see Fig. 2 .
Note that because the entire history and goal is given to the RL agent, the environment is Markovian.
Reward We have a terminal reward of +1 only if the goal g is achieved and 0 otherwise, i.e, it is +1 if the environment agent takes the goal action g. The episode ends after n steps.
In our experiments we consider n = 1 and n = 3.
In this section we describe the models for M env and M RL .
In this work these are retrieval models, using the LIGHT dialogue corpus as candidates.
We leave generative models to future work.
Base Agent Architecture For all our models we adopt the same base architecture, which is a 12-layer bidirectional transformer (Vaswani et al., 2017) pre-trained on a large dialogue corpus (Reddit, 174M examples), and then fine-tuned on our task 1 .
To score retrieval candidates, we use a biencoder as in Urbanek et al., 2019) .
That is, two transformers are used, one to encode the context, and another to encoder a candidate dialogue, and a dot product between the first output vector of each scores the match.
To produce a dialogue utterance one then takes the utterance with the largest output from the training set candidates (111k in this case).
For emotes and actions, the same procedure is used, but with those candidate sets instead.
For actions, the candidates are the set of admissible actions at that game state, which are provided by the game engine, for example get apple is only available in the candidate set if it is a valid action (an apple is present in the room).
For emotes, all 22 candidates are always available.
To train the model, a cross entropy loss is used.
Similar to Mazaré et al. (2018) , during training we consider the other elements of the batch as negatives.
Environment agent The environment agent is the base agent described above, and stays fixed during episodes where the RL agent is trained.
This helps guarantee that our RL models stick to using the semantics of natural language (English) rather than so-called language drift, of learning a new emergent language with the same tokens (Lee et al., 2019) .
RL agents We design two RL approaches for our tasks -learn to pick the right latent discrete variables (topics) that lead to the correct U RL i ; and learn to pick the correct U RL i from the top K candidates.
These are described in more detail in Sections 4.2 and 4.3.
We also discuss a baseline "inverse" model trained via behavioral cloning on the human-human data.
We consider an inverse model, trained to imitate human actions given a goal, as both a baseline for comparing to RL models, and for producing weights form which we can fine-tune.
The inverse model consists of a Bi-encoder, as described above, which takes as input an observation O t similar to our RL models, and outputs an utterance.
We train it by extracting from the human-human game logs training set (which does not have goals) every instance where a game action occurs at time t in S t , that is where
for 0 < i < t might be null).
We then construct a training example for the inverse model with observation (D RL , g = A env t , S t−1 ).
i.e. setting the goal g to be A env t , and with the desired action to be taken by the agent as U RL t .
Here we use the subscripts "RL" and "env" just to mark the relative positions in the sequence, as all actions and utterances come from the human logs.
Note also that unlike the RL agents we train, the human in the RL agent "position" can take game actions.
We can thus train this model in a supervised manner using a cross entropy loss as described before.
This model does not learn a policy interactively, and hence might not learn to plan or strategize optimally for goal completion.
The data distribution it is trained on is different than the data distribution seen by the RL agents.
Nevertheless, it can serve as a strong baseline.
Further, when training our RL agents, we initialize their weights to the weights of this model, and then fine-tune from that point.
Optimizing all the parameters of a large transformer architecture by RL is both incredibly costly in data efficiency and computing time, and is also known to have the problem of language drift (Lee et al., 2019) -that is, there is no guarantee after training with self-chat that the models will output recognizable natural language utterances.
A solution to both problems is to train most of the parameters of the model with human-human language data, and then to either disentangle or only optimize some of the parameters with model self-chat .
Here, we propose a straight-forward model for that purpose.
We assume an RL agent that consists of two components.
The first component F c (O) = P (T c (O)) maps from an observation to a discrete variable with C possible values.
It consists of a chain of two functions: a transformer T s that takes in the observation, and outputs a state representations, and a policy chooser c = P (s) ∈ (1, . . .
, C) which takes in the state representation and outputs the value of the discrete latent variable.
The second component T u (O, c) is an additional transformer that takes as input the observation as well as the output of the first component, and outputs a dialogue utterance.
That is, the entire model is the chain u = T u (O, P (T s (O))).
We make this explicit decomposition so that we can train only part of the model with RL; note that the "action" trained via RL is choosing c, not outputting the final utterance.
Initial topics We first pre-train the transformer T s using the inverse model described in Section 4.1, which produces a vectorial representation of a given observation.
We then run K-means over the vectorial representations of all observations from the training set to provide the mapping to one of C values, which represent dialogue topics, which we use as our initial function P (s).
These two functions together give us our initialization of F c .
Table 1 shows the cluster ID and the topic denoted by that cluster along with the most representative sentences (closest to the center) for that cluster for 50 topics.
As we can see, the clusters learnt can be coherent about a topic.
We use these 50 topics as a set of actions A for our RL setup.
From c to A Given our initial choice of F c , we can also pre-train T u .
We simply take our initial human-human training data, and for each observation append the topic computed by F c to it.
This allows our model to be able to generate an action (utterance) conditional on both an input and a topic.
We can now train a policy by RL that optimizes the topic at any given point in the episode.
We keep the pre-trained portions of the model T u and T s fixed and during fine-tuning only optimize P .
The cluster chooser P is redefined (from the initial K-means) to be an MLP network consisting of 2 layers.
A discrete action is sampled from a categorical probability distribution over the possible topics, given by c t ∼ Categorical(h 2 t ), where h 2 t = tanh(W 2 tanh(W 1 s t + b 1 ) + b 2 ).
The state vector s t also encodes the goal g and hence, the policy is conditioned on the goal g of the agent.
Hence, the policy can learn strategies that will result in picking actions at each time step t that will help the agent to achieve its goal g. As our RL agent can only choose topics, it cannnot redefine easily the meaning of words to cause language drift.
The Top-K model is another approach to keeping the number of trainable parameters small.
It uses the inverse model to get a context embedding v context from the observation, and a list of K candidate utterance embeddings v 1 , ...v K .
These are the encodings by the inverse model of the K utterances it considers most likely given the context and goal.
We then train a small (2-layer) transformer model that takes as input the set {v context , v 1 , ...v K }.
We use the attention above weights of v context against the candidates at the last layer of the transformer as the distribution over the candidates for sampling an utterance.
We use K = 50 in the experiments.
We use the Advantage Actor-Critic implementation (A2C; Kostrikov, 2018) to train the policy and the value function for both the latent-variable and top-K models.
Chit-chat dialogue There is an increasing body of work in the domain of chit-chat, where the primary approaches being currently tried are end-to-end neural approaches.
They are typically large pre-trained and then fine-tuned transformers, either generative or retrieval, where currently retrieval models work best on a number of tasks (Zhang et al., 2018; Li et al., 2019) .
Our work shares a commonality with these approaches in that the original LIGHT dialogue data we use has no specified goals, and humans chit-chat together (and act).
Thus, the conversations cover a rich number of diverse topics.
In Urbanek et al. (2019) models were trained in a similar fashion to chit-chat task models, and we adopt similar architectures here, but instead adapt them to learn to pursue goals.
Goal-oriented dialogue Traditional goal-oriented dialogue has focused on narrow tasks that would typically be useful for a dialogue-based assistant, for example restaurant (Henderson et al., 2014) , taxi, train, and hotel (Budzianowski et al., 2018) or trip (El Asri et al., 2017) booking.
Hence, each task typically focuses on a narrow slice of natural language and world knowledge for a specialized domain.
Earlier work focused on labeled state representations, slot filling mechanisms and dialogue managers (Rieser & Lemon, 2011) , and more recent work has shifted to an end-to-end approach (Bordes et al., 2017) , in line with chit-chat models, but still the two sets of tasks are rarely considered together, or by using the same methods.
RL for dialogue The classical goal-oriented dialogue literature studies RL extensively (Singh et al., 2000) .
Typically, they used RL to improve dialogue managers, which manage transitions between dialogue states (Singh et al., 2002; Pietquin et al., 2011; Rieser & Lemon, 2011; Gasic et al., 2013; Fatemi et al., 2016) .
Recent works have focused more on end-to-end learning.
Some works have focused on self-play type mechanisms for end-to-end reinforcement learning, where the reward is derived from goal.
A related approach to ours is the negotation tasks of ; , which require two agents to swap 3 item types (hats, balls, books) where the value of the items is different for the two agents, and derives their personal reward.
In contrast, our setup encompasses a rich world of settings and characters -with 3462 object types, and a corresponding large number of actions.
This is reflected in the vocabulary size itself (∼32,000 versus ∼2,000 in the negotation tasks).
Other notable uses of RL in dialogue include within visual question answering (Das et al., 2017) , in the domain of chit-chat where RL has been used to decrease repetitive and generic responses through the the use of self-play (Li et al., 2016b) , and through human-bot conversation (Sankar & Ravi, 2019) .
RL for language and games RL is used extensively for learning to play games, one of the most well known examples being AlphaGo (Silver et al., 2016) .
Since then, language in games has started to be more deeply explored, for example in graphical games such as Minecraft (Oh et al., 2017) , Real-time strategy war games (Hu et al., 2019) , or in text adventure games (Narasimhan et al., 2015; Côté et al., 2018) .
The latter are related to our setting.
However, those approaches use RL to optimize the set of actions given feedback in a single-player rather than multi-player game, so the text only refers to the environment, and there is no dialogue or actions from other agents.
Our work focuses specifically on the latter.
We compare our various models on the game action and emote action tasks.
We experiment with differing number of steps n allowed to complete the goal, n = 1 and n = 3.
Our main results for both seen and unseen test environments are given in Table 2 .
We report the average reward and for n = 3 the average number of turns before completion.
The results show clear improvements for our topic RL ( §4.2) and top-K RL ( §4.3) compared to the inverse model baseline for all values of n, and both types of actions (game actions and emotes).
We show the training curves for topic RL in Fig. 3 , reporting rewards averaged over the batch (512 for n = 1, and 128 for n = 3).
They show relatively smooth improvements over time, with clear gains over the baseline.
As a sanity check we also tried, after training, to replace the topic RL policy with random topic prediction, which yielded poor results, e.g. 0.217 reward for n = 1 test seen game actions.
Our model is clearly learning appropriate topic acts.
We show examples of successful utterances, achieving goal actions in Fig. 3 for a diverse range of scenarios, actions and language.
(n = 1) (n = 3) (n = 1) (n = 3)
In this paper, we investigate agents that can interact (speak or act) and can achieve goals in a rich world with diverse language, bridging the gap between chit-chat and goal-oriented dialogue.
We achieve this by defining a task for an agent, where the goal is for the other player to execute a particular action.
We explore two reinforcement learning based approaches to solve this task: the policy either learns to pick a topic or learns to pick an utterance given the top K utterances, and compare them against a strong baseline trained to imitate chit-chat.
We show that these approaches effectively learn dialogue strategies that lead to successful completion of goals, while producing natural chat.
Future work should explore further RL algorithms for agents that can act and speak in natural language at scale in our proposed rich task environment, and we expect further advancements.
Constraints Outcome get object actor and object in same room actor is carrying object object is gettable drop object actor is carrying object object is in room object is gettable get object1 from object2
Actor and object2 in same room actor is carrying object1 object1 is gettable object2 is surface or container object2 is carrying object1 put object1 in/on object2 Actor and object2 in same room object2 is carrying object1 object2 is container or surface actor is carrying object1
give object to agent Actor and agent in same room agent is carrying object object is a member of actor steal object from agent actor and agent in same room actor is carrying object object is a member of agent hit agent Actor and agent in same room inform agent of attack hug agent Actor and agent in same room inform agent of hug drink object actor is carrying object inform actor of drinking successfully object is a drink eat object actor is carrying object inform actor of eating successfully object is a food wear object actor is carrying object actor is wearing object object is wearable wield object actor is carrying object actor is wielding object object is a weapon remove object actor is wearing/wielding object actor is carrying object object is wearable or a weapon Table 4 : LIGHT actions and constraints from Urbanek et al. (2019) B GAME EMOTES WITHIN LIGHT applaud, blush, cry, dance, frown, gasp, grin, groan, growl, laugh, nod, nudge, ponder, pout, scream, shrug, sigh, smile, stare, wave, wink, yawn Figure 4: Emote actions within the LIGHT platform from Urbanek et al. (2019)

assistant: text: This paper explores the goal-oriented dialogue setting with reinforcement learning in a Fantasy Text Adventure Game and observes that the RL approaches outperform supervised learning models.