Reinforcement learning (RL) has been successful in a variety of areas such as continuous control BIBREF0, dialogue systems BIBREF1, and game-playing BIBREF2. However, RL adoption in real-world problems is limited due to poor sample efficiency and failure to generalise to environments even slightly different from those seen during training. We explore language-conditioned policy learning, where agents use machine reading to discover strategies required to solve a task, thereby leveraging language as a means to generalise to new environments.Prior work on language grounding and language-based RL (see BIBREF3 for a recent survey) are limited to scenarios in which language specifies the goal for some fixed environment dynamics BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, or the dynamics of the environment vary and are presented in language for some fixed goal BIBREF9. In practice, changes to goals and to environment dynamics tend to occur simultaneously—given some goal, we need to find and interpret relevant information to understand how to achieve the goal. That is, the agent should account for variations in both by selectively reading, thereby generalising to environments with dynamics not seen during training.Our contributions are two-fold. First, we propose a grounded policy learning problem that we call (). In , the agent must jointly reason over a language goal, a document that specifies environment dynamics, and environment observations. In particular, it must identify relevant information in the document to shape its policy and accomplish the goal. To necessitate reading comprehension, we expose the agent to ever changing environment dynamics and corresponding language descriptions such that it cannot avoid reading by memorising any particular environment dynamics. We procedurally generate environment dynamics and natural language templated descriptions of dynamics and goals to produced a combinatorially large number of environment dynamics to train and evaluate .Second, we propose to model the joint reasoning problem in . We show that generalises to goals and environment dynamics not seen during training, and outperforms previous language-conditioned models such as language-conditioned CNNs and FiLM BIBREF10, BIBREF6 both in terms of sample efficiency and final win-rate on . Through curriculum learning where we adapt trained on simpler tasks to more complex tasks, we obtain agents that generalise to tasks with natural language documents that require five hops of reasoning between the goal, document, and environment observations. Our qualitative analyses show that attends to parts of the document relevant to the goal and environment observations, and that the resulting agents exhibit complex behaviour such as retrieving correct items, engaging correct enemies after acquiring correct items, and avoiding incorrect enemies. Finally, we highlight the complexity of in scaling to longer documents, richer dynamics, and natural language variations. We show that significant improvement in language-grounded policy learning is needed to solve these problems in the future.A growing body of research is learning policies that follow imperative instructions. The granularity of instructions vary from high-level instructions for application control BIBREF11 and games BIBREF5, BIBREF6 to step-by-step navigation BIBREF7. In contrast to learning policies for imperative instructions, BIBREF4, BIBREF9 infer a policy for a fixed goal using features extracted from high level strategy descriptions and general information about domain dynamics. Unlike prior work, we study the combination of imperative instructions and descriptions of dynamics. Furthermore, we require that the agent learn to filter out irrelevant information to focus on dynamics relevant to accomplishing the goal.Language grounding refers to interpreting language in a non-linguistic context. Examples of such context include images BIBREF12, games BIBREF13, BIBREF14, robot control BIBREF15, BIBREF16, and navigation BIBREF17. We study language grounding in interactive games similar to BIBREF11, BIBREF5 or BIBREF8, where executable semantics are not provided and the agent must learn through experience. Unlike prior work, we require grounding between an underspecified goal, a document of environment dynamics, and world observations. In addition, we focus on generalisation to not only new goal descriptions but new environments dynamics.We consider a scenario where the agent must jointly reason over a language goal, relevant environment dynamics specified in a text document, and environment observations. In reading the document, the agent should identify relevant information key to solving the goal in the environment. A successful agent needs to perform this language grounding to generalise to new environments with dynamics not seen during training.To study generalisation via reading, the environment dynamics must differ every episode such that the agent cannot avoid reading by memorising a limited set of dynamics. Consequently, we procedurally generate a large number of unique environment dynamics (e.g. effective(blessed items, poison monsters)), along with language descriptions of environment dynamics (e.g. blessed items are effective against poison monsters) and goals (e.g. Defeat the order of the forest). We couple a large, customisable ontology inspired by rogue-like games such as NetHack or Diablo, with natural language templates to create a combinatorially rich set of environment dynamics to learn from and evaluate on.In , the agent is given a document of environment dynamics, observations of the environment, and an underspecified goal instruction. Figure FIGREF3 illustrates an instance of the game. Concretely, we design a set of dynamics that consists of monsters (e.g. wolf, goblin), teams (e.g. Order of the Forest), element types (e.g. fire, poison), item modifiers (e.g. fanatical, arcane), and items (e.g. sword, hammer). When the player is in the same cell with a monster or weapon, the player picks up the item or engages in combat with the monster. The player can possess one item at a time, and drops existing weapons if they pick up a new weapon. A monster moves towards the player with 60% probability, and otherwise moves randomly. The dynamics, the agent's inventory, and the underspecified goal are rendered as text. The game world is rendered as a matrix of text in which each cell describes the entity occupying the cell. We use human-written templates for stating which monsters belong to which team, which modifiers are effective against which element, and which team the agent should defeat (see appendix SECREF13 for details). In order to achieve the goal, the agent must cross-reference relevant information in the document and as well as in the observations.During every episode, we subsample a set of groups, monsters, modifiers, and elements to use. We randomly generate group assignments of which monsters belong to which team and which modifier is effective against which element. A document that consists of randomly ordered statements corresponding to this group assignment is presented to the agent. We sample one element, one team, and a monster from that team (e.g. “fire goblin” from “Order of the forest”) to be the target monster. Additionally, we sample one modifier that beats the element and an item to be the item that defeats the target monster (e.g. “fanatical sword”). Similarly, we sample an element, a team, and a monster from a different team to be the distractor monster (e.g. poison bat), as well as an item that defeats the distractor monster (e.g. arcane hammer).In order to win the game (e.g. Figure FIGREF3), the agent mustidentify the target team from the goal (e.g. Order of the Forest)identify the monsters that belong to that team (e.g. goblin, jaguar, and lynx)identify which monster is in the world (e.g. goblin), and its element (e.g. fire)identify the modifiers that are effective against this element (e.g. fanatical, shimmering)find which modifier is present (e.g. fanatical), and the item with the modifier (e.g. sword)pick up the correct item (e.g. fanatical sword)engage the correct monster in combat (e.g. fire goblin).If the agent deviates from this trajectory (e.g. does not have correct item before engaging in combat, engages with distractor monster), it cannot defeat the target monster and therefore will lose the game. The agent receives a reward of +1 if it wins the game and -1 otherwise.presents challenges not found in prior work in that it requires a large number of grounding steps in order to solve a task. In order to perform this grounding, the agent must jointly reason over a language goal and document of dynamics, as well as environment observations. In addition to the environment, the positions of the target and distractor within the document are randomised—the agent cannot memorise ordering patterns in order to solve the grounding problems, and must instead identify information relevant to the goal and environment at hand.We split environments into train and eval sets. No assignments of monster-team-modifier-element are shared between train and eval to test whether the agent is able to generalise to new environments with dynamics not seen during training via reading. There are more than 2 million train or eval environments without considering the natural language templates, and 200 million otherwise. With random ordering of templates, the number of unique documents exceeds 15 billion.In addition to the main tasks, we also study a simpler formulation called that has a fixed goal. In , the agent must interpret a document that describes the environment dynamics in order to solve the task. Given an set of characters (e.g. a-z), we sample 3 characters and set up a rock-paper-scissors-like dependency graph between the characters (e.g. “a beats b, b beats c, c beats a”). We then spawn a monster in the world with a randomly assigned type (e.g. “b goblin”), as well as an item corresponding to each type (e.g. “a”, “b”, and “c”). The attributes of the agent, monster, and items are set up such that the player must obtain the correct item and then engage the monster in order to win. Any other sequence of actions (e.g. engaging the monster without the correct weapon) results in a loss. The winning policy should then be to first identify the type of monster present, then cross-reference the document to find which item defeats that type, then pick up the item, and finally engage the monster in combat. Figure FIGREF49 shows an instance of .We propose the model, which builds representations that capture three-way interactions between the goal, document describing environment dynamics, and environment observations. We begin with definition of the () layer, which forms the core of our model.Feature-wise linear modulation (FiLM), which modulates visual inputs using representations of textual instructions, is an effective method for image captioning BIBREF10 and instruction following BIBREF6. In , the agent must not only filter concepts in the visual domain using language but filter concepts in the text domain using visual observations. To support this, builds codependent representations of text and visual inputs by further incorporating conditional representations of the text given visual observations. Figure FIGREF12 shows the layer.We use upper-case bold letters to denote tensors, lower-case bold letters for vectors, and non-bold letters for scalars. Exact dimensions of these variables are shown in Table TABREF42 in appendix SECREF8. Let $_$ denote a fixed-length $_$-dimensional representation of the text and $_$ the representation of visual inputs with height $H$, width $W$, and $_$ channels. Let $$ denote a convolution layer. Let + and * symbols denote element-wise addition and multiplication operations that broadcast over spatial dimensions. We first modulate visual features using text features:Unlike FiLM, we additionally modulate text features using visual features:The output of the layer consists of the sum of the modulated features $$, as well as a max-pooled summary $$ over this sum across spatial dimensions.We model interactions between observations from the environment, goal, and document using layers. We first encode text inputs using bidirectional LSTMs, then compute summaries using self-attention and conditional summaries using attention. We concatenate text summaries into text features, which, along with visual features, are processed through consecutive layers. In this case of a textual environment, we consider the grid of word embeddings as the visual features for . The final output is further processed by MLPs to compute a policy distribution over actions and a baseline for advantage estimation. Figure FIGREF18 shows the model.Let $_$ denote word embeddings corresponding to the observations from the environment, where $_[:, :, i, j]$ represents the embeddings corresponding to the $_$-word string that describes the objects in location $(i, j)$ in the grid-world. Let $_$, $_$, and $_$ respectively denote the embeddings corresponding to the $_$-word document, the $_$-word inventory, and the $_$-word goal. We first compute a fixed-length summary $_$ of the the goal using a bidirectional LSTM BIBREF18 followed by self-attention BIBREF19, BIBREF20.We abbreviate self-attention over the goal as $_= (_)$. We similarly compute a summary of the inventory as $_= (_(_))$. Next, we represent the document encoding conditioned on the goal using dot-product attention BIBREF21.We abbreviate attention over the document encoding conditioned on the goal summary as $_= {_}{_}$. Next, we build the joint representation of the inputs using successive layers. At each layer, the visual input to the layer is the concatenation of the output of the previous layer with positional features. For each cell, the positional feature $_$ consists of the $x$ and $y$ distance from the cell to the agent's position respectively, normalized by the width and height of the grid-world. The text input is the concatenation of the goal summary, the inventory summary, the attention over the document given the goal, and the attention over the document given the previous visual summary. Let ${a; b}$ denote the feature-wise concatenation of $a$ and $b$. For the $i$th layer, we have$_{\text{-}}(_)$ is another encoding of the document similar to $_$, produced using a separate LSTM, such that the document is encoded differently for attention with the visual features and with the goal. For $i = 0$, we concatenate the bag-of-words embeddings of the grid with positional features as the initial visual features $^{(0)} = {\sum _j_{, j}; _}$. We max pool a linear transform of the initial visual features to compute the initial visual summary $^{(0)} = (_^{(0)} + _)$. Let $$ denote visual summary of the last layer. We compute the policy $$ and baseline $$ aswhere $_{\rm policy}$ and $_{\rm baseline}$ are 2-layer multi-layer perceptrons with $$ activation. We train using an implementation of IMPALA BIBREF22, which decouples actors from learners and uses V-trace for off-policy correction. Please refer to appendix SECREF10 for details.We consider variants of by varying the size of the grid-world ($6\times 6$ vs $10\times 10$), allowing many-to-one group assignments to make disambiguation more difficult (group), allowing dynamic, moving monsters that hunt down the player (dyna), and using natural language templated documents (nl). In the absence of many-to-one assignments, the agent does not need to perform steps 3 and 5 in section SECREF3 as there is no need to disambiguate among many assignees, making it easier to identify relevant information.We compare to the FiLM model by BIBREF6 and a language-conditioned residual CNN model. We train on one set of dynamics (e.g. group assignments of monsters and modifiers) and evaluated on a held-out set of dynamics. We also study three variants of . In no_task_attn, the document attention conditioned on the goal utterance ((DISPLAY_FORM26)) is removed and the goal instead represented through self-attention and concatenated with the rest of the text features. In no_vis_attn, we do not attend over the document given the visual output of the previous layer ((DISPLAY_FORM27)), and the document is instead represented through self-attention. In no_text_mod, text modulation using visual features ((DISPLAY_FORM14)) is removed. Please see appendix SECREF9 for model details on our model and baselines, and appendix SECREF10 for training details.We compare to baselines and ablated variants on a simplified variant of in which there are one-to-one group assignments (no group), stationary monsters (no dyna), and no natural language templated descriptions (no nl). Figure FIGREF29 shows that compared to baselines and ablated variants, is more sample efficient and converges to higher performance. Moreover, no ablated variant is able to solve the tasks—it is the combination of ablated features that enables to win consistently. Qualitatively, the ablated variants converge to locally optimum policies in which the agent often picks up a random item and then attacks the correct monster, resulting in a $\sim 50$% win rate. Table FIGREF29 shows that all models, with the exception of the CNN baseline, generalise to new evaluation environments with dynamics and world configurations not seen during training, with outperforming FiLM and the CNN model. We find similar results for , its ablated variants, and baselines on other tasks (see appendix SECREF11 for details).Due to the long sequence of co-references the agent must perform in order to solve the full ($10\times 10$ with moving monsters, many-to-one group assignments, and natural language templated documents) we design a curriculum to facilitate policy learning by starting with simpler variants of . We start with the simplest variant (no group, no dyna, no nl) and then add in an additional dimension of complexity. We repeatedly add more complexity until we obtain $10\times 10$ worlds with moving monsters, many-to-one group assignments and natural language templated descriptions. The performance across the curriculum is shown in Table TABREF32 (see Figure FIGREF58 in appendix SECREF12 for training curves of each stage). We see that curriculum learning is crucial to making progress on , and that initial policy training (first row of Table TABREF32) with additional complexities in any of the dimensions result in significantly worse performance. We take each of the 5 runs after training through the whole curriculum and evaluate them on dynamics not seen during training. Table TABREF33 shows variants of the last stage of the curriculum in which the model was trained on $6\times 6$ versions of the full and in which the model was trained on $10\times 10$ versions of the full . We see that models trained on smaller worlds generalise to bigger worlds. Despite curriculum learning, however, performance of the final model trail that of human players, who can consistently solve . This highlights the difficulties of the problem and suggests that there is significant room for improvement in developing better language grounded policy learners.Figure FIGREF34 shows attention conditioned on the goal and on observation summaries produced by intermediate layers. Goal-conditioned attention consistently locates the clause that contains the team the agent is supposed to attack. Intermediate layer attentions focus on regions near modifiers and monsters, particularly those that are present in the observations. These results suggests that attention mechanisms in help identify relevant information in the document.We examine trajectories from well-performing policies (80% win rate) as well as poorly-performing policies (50% win rate) on the full . We find that well-performing policies exhibit a number of consistent behaviours such as identifying the correct item to pick up to fight the target monster, avoiding distractors, and engaging target monsters after acquiring the correct item. In contrast, the poorly-performing policies occasionally pick up the wrong item, causing the agent to lose when engaging with a monster. In addition, it occasionally gets stuck in evading monsters indefinitely, causing the agent to lose when the time runs out. Replays of both policies can be found in GIFs in the supplementary materials.We proposed , a grounded policy learning problem in which the agent must jointly reason over a language goal, relevant dynamics specified in a document, and environment observations. In order to study , we procedurally generated a combinatorially large number of environment dynamics such that the model cannot memorise a set of environment dynamics and must instead generalise via reading. We proposed , a model that captures three-way interactions between the goal, document, and observations, and that generalises to new environments with dynamics not seen during training. outperforms baselines such as FiLM and language-conditioned CNNs. Through curriculum learning, performs well on complex tasks that require several reasoning and coreference steps with natural language templated goals and descriptions of the dynamics. Our work suggests that language understanding via reading is a promising way to learn policies that generalise to new environments. Despite curriculum learning, our best models trail performance of human players, suggesting that there is ample room for improvement in grounded policy learning on complex problems. In addition to jointly learning policies based on external documentation and language goals, we are interested in exploring how to use supporting evidence in external documentation to reason about plans BIBREF23 and induce hierarchical policies BIBREF24, BIBREF25.These figures shows key snapshots from a trained policy on randomly sampled environments.Let $_\in {_}$ denote a fixed-length $_$-dimensional representation of the text and $_\in {_\times H \times W}$ denote the representation of visual inputs withThe used in our experiments consists of 5 consecutive layers, each with 3x3 convolutions and padding and stride sizes of 1. The layers have channels of 16, 32, 64, 64, and 64, with residual connections from the 3rd layer to the 5th layer. The Goal-doc LSTM (see Figure FIGREF18) shares weight with the Goal LSTM. The Inventory and Goal LSTMs have a hidden dimension of size 10, whereas the Vis-doc LSTM has a dimension of 100. We use a word embedding dimension of 30.Like , the CNN baseline consists of 5 layers of convolutions with channels of 16, 32, 64, 64, and 64. There are residual connections from the 3rd layer to the 5th layer. The input to each layer consists of the output of the previous layer, concatenated with positional features.The input to the network is the concatenation of the observations $^{(0)}$ and text representations. The text representations consist of self-attention over bidirectional LSTM-encoded goal, document, and inventory. These attention outputs are replicated over the dimensions of the grid and concatenated feature-wise with the observation embeddings in each cell. Figure FIGREF46 illustrates the CNN baseline.The FiLM baseline encodes text in the same fashion as the CNN model. However, instead of using convolutional layers, each layer is a FiLM layer from BIBREF6. Note that in our case, the language representation is a self-attention over the LSTM states instead of a concatenation of terminal LSTM states.We train using an implementation of IMPALA BIBREF22. In particular, we use 20 actors and a batch size of 24. When unrolling actors, we use a maximum unroll length of 80 frames. Each episode lasts for a maximum of 1000 frames. We optimise using RMSProp BIBREF26 with a learning rate of 0.005, which is annealed linearly for 100 million frames. We set $\alpha = 0.99$ and $\epsilon = 0.01$.During training, we apply a small negative reward for each time step of $-0.02$ and a discount factor of 0.99 to facilitate convergence. We additionally include a entropy cost to encourage exploration. Let $$ denote the policy. The entropy loss is calculated asIn addition to policy gradient, we add in the entropy loss with a weight of 0.005 and the baseline loss with a weight of 0.5. The baseline loss is computed as the root mean square of the advantages BIBREF22.When tuning models, we perform a grid search using the training environments to select hyperparameters for each model. We train 5 runs for each configuration in order to report the mean and standard deviation. When transferring, we transfer each of the 5 runs to the new task and once again report the mean and standard deviation.We split environment dynamics by permuting 3-character dependency graphs from an alphabet, which we randomly split into training and held-out sets. This corresponds to the “permutations” setting in Table TABREF50.We train models on the $10\times 10$ worlds from the training set and evaluate them on both seen and not seen during training. The left of Figure FIGREF51 shows the performance of models on worlds of varying sizes with training environment dynamics. In this case, the dynamics (e.g. dependency graphs) were seen during training. For $9\times 9$ and $11\times 11$ worlds, the world configuration not seen during training. For $10\times 10$ worlds, there is a 5% chance that the initial frame was seen during training. Figure FIGREF51 shows the performance on held-out environments not seen during training. We see that all models generalise to environments not seen during training, both when the world configuration is not seen (left) and when the environment dynamics are not seen (right).In addition to splitting via permutations, we devise two additional ways of splitting environment dynamics by introducing new edges and nodes into the held-out set. Table TABREF50 shows the three different settings. For each, we study the transfer behaviour of models on new environments. Figure FIGREF52 shows the learning curve when training a model on the held-out environments directly and when transferring the model trained on train environments to held-out environments. We observe that all models are significantly more sample-efficient when transferring from training environments, despite the introduction of new edges and new nodes.In Figure FIGREF51, we see that the FiLM model outperforms the CNN model on both training environment dynamics and held-out environment dynamics. further outperforms FiLM, and does so more consistently in that the final performance has less variance. This behaviour is also observed in the in Figure FIGREF52. When training on the held-out set without transferring, is more sample efficient than FiLM and the CNN model, and achieves higher win-rate. When transferring to the held-out set, remains more sample efficient than the other models.We collect human-written natural language templates for the goal and the dynamics. The goal statements in describe which team the agent should defeat. We collect 12 language templates for goal statements. The document of environment dynamics consists of two types of statements. The first type describes which monsters are assigned to with team. The second type describes which modifiers, which describe items, are effective against which element types, which are associated with monsters. We collection 10 language templates for each type of statements. The entire document is composed from statements, which are randomly shuffled. We randomly sample a template for each statement, which we fill with the monsters and team for the first type and modifiers and element for the second type.

Q: How is data for RTFM collected?

Q: How better is performance of proposed model compared to baselines?
A: Proposed model achive 66+-22 win rate, baseline CNN 13+-1  and baseline FiLM 32+-3 .

Q: How does propose model model that capture three-way interactions?