Title: CONTINGENCY-AWARE EXPLORATION IN REINFORCEMENT LEARNING

Abstract: This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning. To investigate this question, we consider an instantiation of this hypothesis evaluated on the Arcade Learning Element (ALE). In this study, we develop an attentive dynamics model (ADM) that discovers controllable elements of the observations, which are often associated with the location of the character in Atari games. The ADM is trained in a self-supervised fashion to predict the actions taken by the agent. The learned contingency information is used as a part of the state representation for exploration purposes. We demonstrate that combining actor-critic algorithm with count-based exploration using our representation achieves impressive results on a set of notoriously challenging Atari games due to sparse rewards. 1 For example, we report a state-of-the-art score of >11,000 points on MONTEZUMA'S REVENGE without using expert demonstrations, explicit high-level information (e.g., RAM states), or supervisory data. Our experiments confirm that contingency-awareness is indeed an extremely powerful concept for tackling exploration problems in reinforcement learning and opens up interesting research questions for further investigations.

1. INTRODUCTION:

The success of reinforcement learning (RL) algorithms in complex environments hinges on the way they balance exploration and exploitation. There has been a surge of recent interest in developing effective exploration strategies for problems with high-dimensional state spaces and sparse rewards (Schmidhuber, 1991; Oudeyer & Kaplan, 2009; Houthooft et al., 2016; Bellemare et al., 2016; Osband et al., 2016; Pathak et al., 2017; Plappert et al., 2018; Zheng et al., 2018) . Deep neural networks have seen great success as expressive function approximators within RL and as powerful representation learning methods for many domains. In addition, there have been recent studies on using neural network representations for exploration (Tang et al., 2017; Martin et al., 2017; Pathak et al., 2017) . For example, count-based exploration with neural density estimation (Bellemare et al., 2016; Tang et al., 2017; Ostrovski et al., 2017) presents one of the state-of-the-art techniques on the most challenging Atari games with sparse rewards.

Despite the success of recent exploration methods, it is still an open question on how to construct an optimal representation for exploration. For example, the concept of visual similarity is used for learning density models as a basis for calculating pseudo-counts (Bellemare et al., 2016; Ostrovski et al., 2017) . However, as Tang et al. (2017) noted, the ideal way to represent states should be based on what is relevant to solving the MDP, rather than only relying on visual similarity. In addition, there remains another question on whether the representations used for recent exploration works are easily interpretable. To address these questions, we investigate whether we can learn a complementary, more intuitive, and interpretable high-level abstraction that can be very effective in exploration by using the ideas of contingency awareness and controllable dynamics.

The key idea that we focus on in this work is the notion of contingency awareness (Watson, 1966; Bellemare et al., 2012) -the agent's understanding of the environmental dynamics and recognizing that some aspects of the dynamics are under the agent's control. Intuitively speaking, this can represent the segmentation mask of the agent operating in the 2D or 3D environments (yet one can think of more abstract and general state spaces). In this study, we investigate the concept of contingency awareness based on self-localization, i.e., the awareness of where the agent is located in the abstract state space. We are interested in discovering parts of the world that are directly dependent on the agent's immediate action, which often reveal the agent's approximate location.

For further motivation on the problem, we note that contingency awareness is a very important concept in neuroscience and psychology. In other words, being self-aware of one's location is an important property within many observed intelligent organisms and systems. For example, recent breakthroughs in neuroscience, such as the Nobel Prize winning work on the grid cells (Moser et al., 2015; Banino et al., 2018) , show that organisms that perform very well in spatially-challenging tasks are self-aware of their location. This allows rats to navigate, remember paths to previously visited places and important sub-goals, and find shortcuts. In addition, the notion of contingency awareness has been shown as an important factor in developmental psychology (Watson, 1966; Baeyens et al., 1990) . We can think of self-localization (and more broadly self-awareness) as a principled and fundamental direction towards intelligent agents.

Based on these discussions, we hypothesize that contingency awareness can be a powerful mechanism for tackling exploration problems in reinforcement learning. We consider an instantiation of this hypothesis evaluated on the Arcade Learning Element (ALE). For example, in the context of 2D Atari games, contingency-awareness roughly corresponds to understanding the notion of controllable entities (e.g., the player's avatar), which Bellemare et al. (2012) refer to as contingent regions. More concretely, as shown in Figure 1 , in the game FREEWAY, only the chicken sprite is under the agent's control and not the multiple moving cars; therefore the chicken's location should be an informative element for exploration (Bellemare et al., 2012; Pathak et al., 2017) .

In this study, we also investigate whether contingency awareness can be learned without any external annotations or supervision. For this, we provide an instantiation of an algorithm for automatically learning such information and using it for improving exploration on a 2D ALE environment (Bellemare et al., 2013) . Concretely, we employ an attentive dynamics model (ADM) to predict the agent's action chosen between consecutive states. It allows us to approximate the agent's position in 2D environments, but unlike other approaches such as (Bellemare et al., 2012) , it does not require any additional supervision to do so. The ADM learns in an online and self-supervised fashion with pure observations as the agent's policy is updated and does not require hand-crafted features, an environment simulator, or supervision labels for training.

In experimental evaluation, our methods significantly improve the performance of A2C on hardexploration Atari games in comparison with competitive methods such as density-based exploration (Bellemare et al., 2016; Ostrovski et al., 2017) and SimHash (Tang et al., 2017) . We report very strong results on sparse-reward Atari games, including the state-of-the-art performance on the notoriously difficult MONTEZUMA'S REVENGE, when combining our proposed exploration strategy with PPO (Schulman et al., 2017) , without using expert demonstrations, explicit high-level information (e.g., RAM states), or resetting the environment to an arbitrary state.

We summarize our contributions as follows:

• We demonstrate the importance of learning contingency awareness for efficient exploration in challenging sparse-reward RL problems. • We develop a novel instance of attentive dynamics model using contingency and controllable dynamics to provide robust localization abilities across the most challenging Atari environments. • We achieve a strong performance on difficult sparse-reward Atari games, including the state-ofthe-art score on the notoriously challenging MONTEZUMA'S REVENGE.

Overall, we believe that our experiments confirm the hypothesis that contingency awareness is an extremely powerful concept for tackling exploration problems in reinforcement learning, which opens up interesting research questions for further investigations. 

2. RELATED WORK:

Self-Localization. The discovery of grid cells (Moser et al., 2015) motivates working on agents that are self-aware of their location. Banino et al. (2018) emphasize the importance of self-localization and train a neural network which learns a similar mechanism to grid cells to perform tasks related to spatial navigation. The presence of grid cells is correlated with high performance. Although grid cells seem tailored to 2D or 3D problems that animals encounter in their life, it is speculated that their use can be extended to more abstract spaces. A set of potential approaches to self-localization ranges from ideas specific to a given environment, e.g., SLAM (Durrant-Whyte & Bailey, 2006 ), to methods with potential generalizability (Mirowski et al., 2017; Jaderberg et al., 2017; Mirowski et al., 2018) .

Self-supervised Dynamics Model and Controllable Dynamics. Several works have used forward and/or inverse dynamics models of the environment (Oh et al., 2015; Agrawal et al., 2016; Shelhamer et al., 2017) . Pathak et al. (2017) employ a similar dynamics model to learn feature representations of states that captures controllable aspects of the environment. This dense representation is used to design a curiosity-driven intrinsic reward. The idea of learning representations on relevant aspects of the environment by learning auxiliary tasks is also explored in (Jaderberg et al., 2017; Bengio et al., 2017; Sawada, 2018) . Our presented approach is different as we focus on explicitly discovering controllable aspects using an attention mechanism, resulting in better interpretability.

Exploration and Intrinsic Motivation. The idea of providing an exploration bonus reward depending on the state-action visit-count was proposed by Strehl & Littman (2008) Martin et al. (2017) also construct a visitation density model over a compressed feature space rather than the raw observation space. Alternatively, Tang et al. (2017) propose a localitysensitive hashing (LSH) method to cluster states and maintain a state-visitation counter based on a form of similarity between frames. We train an agent with a similar count-based exploration bonus, but the way of maintaining state counter seems relatively simpler in that key feature information (i.e., controllable region) is explicitly extracted from the observation and directly used for counting states. Another popular family of exploration strategies in RL uses intrinsic motivation (Schmidhuber, 1991; Singh et al., 2004; Oudeyer & Kaplan, 2009; Barto, 2013) . These methods encourage the agent to look for something surprising in the environment which motivates its search for novel states, such as surprise (Achiam & Sastry, 2017) , curiosity (Pathak et al., 2017; Burda et al., 2018) , and diversity (Eysenbach et al., 2018) , or via feature control (Jaderberg et al., 2017; Dilokthanakul et al., 2017) .

3. APPROACH:



3.1. DISCOVERING CONTINGENCY VIA ATTENTIVE DYNAMICS MODEL:

To discover the region of the observation that is controllable by the agent, we develop an instance of attentive dynamics model (ADM) based on inverse dynamics f inv . The model takes two consecutive input frames (observations) s t-1 , s t ∈ S as input and aims to predict the action (a t-1 ∈ A) taken by the agent to transition from s t-1 to s t : a t-1 = f inv (s t-1 , s t ).

(1) Our key intuition is that the inverse dynamics model should attend to the most relevant part of the observation, which is controllable by the agent, to be able to classify the actions. We determine whether each region in a H × W grid is controllable, or in other words, useful for predicting the agent's action, by using a spatial attention mechanism (Bahdanau et al., 2015; Xu et al., 2015) . An overview of the model is shown in Figure 1 .

Model. To perform action classification, we first compute a convolutional feature map φ s t = φ(s t ) ∈ R H×W ×K based on the observation s t using a convolutional neural network φ. We estimate a set of logit (score) vectors, denoted e t (i, j) ∈ R |A| , for action classification from each grid cell (i, j) of the convolutional feature map. The local convolution features and feature differences for consecutive frames are fed into a shared multi-layer perceptron (MLP) to derive the logits as:

EQUATION

We then compute an attention mask α t ∈ R H×W corresponding to frame t, which indicates the controllable parts of the observation s t . Such attention masks are computed via a separate MLP from the features of each region (i, j), and then converted into a probability distribution using softmax or sparsemax operators (Martins & Astudillo, 2016):

EQUATION

so that i,j α t (i, j) = 1. The sparsemax operator is similar to softmax but yields a sparse attention, leading to more stable performance. Finally, the logits e t (i, j) from all regions are linearly combined using the attention probabilities α t :

EQUATION

Training. The model can be optimized with the standard cross-entropy loss L action (a * t-1 , a t-1 ) with respect to the ground-truth action a * t-1 ∈ A that the agent actually has taken. Based on this formulation, the attention probability α t (i, j) should be high only on regions (i, j) that are predictive of the agent's actions. Our formulation enables learning to localize controllable entities in a selfsupervised way without any additional supervisory signal, unlike some prior work (e.g., (Bellemare et al., 2012) ) that adopts simulators to collect extra supervisory labels.

Optimizing the parameters of ADM on on-policy data is challenging for several reasons. First, the ground-truth action may be unpredictable for given pairs of frames, leading to noisy labels. For example, actions taken in uncontrollable situations do not have any effect (e.g., when the agent is in the middle of jumping in MONTEZUMA'S REVENGE). Second, since we train the ADM online along with the policy, the training examples are not independently and identically distributed, and the data distribution can shift dramatically over time. Third, the action distribution from the agent's policy can run into a low entropy2 , being biased towards certain actions. These issues may prevent the ADM from generalization to novel observations, which hurts exploration. Generally, we prefer models that quickly adapt to the policy and learn to localize the controllable regions in a robust manner.

To mitigate the aforementioned issues, we adopt a few additional objective functions. We encourage the attention distribution to attain a high entropy by including an attention entropy regularization loss, i.e., L ent = -H(α t ). This term penalizes over-confident attention masks, making the attention closer to uniform whenever action prediction is not possible. We also train the logits corresponding to each grid cell independently using a separate cross-entropy loss: p( a i,j t-1 | e t (i, j)) = softmax(e t (i, j)). These additional cross-entropy losses, denoted L i,j cell , allow the model to learn from unseen observations even when attention fails to perform well at first. The entire training objective becomes:

L ADM = L action + i,j L i,j cell + λ ent L ent (5)

where λ ent is a mixing hyperparameter.

3.2. COUNT-BASED EXPLORATION WITH CONTINGENT REGIONS:

One natural way to take advantage of discovered contingent regions for exploration is count-based exploration. The ADM can be used to localize the controllable entity (e.g., the agent's avatar) from an observation s t experienced by the agent. In 2D environments, a natural discretization (x, y) = argmax (j,i) α t (i, j) provides a good approximation of the agent's location within the current observation3 . This provides a key piece of information about the current state of the agent.

Inspired by previous work (Bellemare et al., 2016; Tang et al., 2017) , we add an exploration bonus of r + to the environment reward, where r + (s) = 1/ #(ψ(s)) and #(ψ(s)) denotes the visitation count of the (discrete) mapped state ψ(s), which consists of the contingent region (x, y). We want to find a policy π that maximizes the expected discounted sum of environment rewards r ext plus count-based exploration rewards r

+ , denoted R = E π t γ t (β 1 r ext (s t , a t ) + β 2 r + (s t ))

, where β 1 , β 2 ≥ 0 are hyperparameters that balance the weight of environment reward and exploration bonus. For every state s t encountered at time step t, we increase the counter value #(ψ(s t )) by 1 during training. The full procedure is summarized in Algorithm 1 in Appendix A.

4. EXPERIMENTS:

In the experiments below we investigate the following key questions:

• Does the contingency awareness in terms of self-localization provide a useful state abstraction for exploration? • How well can the self-supervised model discover the ground-truth abstract states? • How well does the proposed exploration strategy perform against other exploration methods?

4.1. EXPERIMENTS WITH A2C:

We evaluate the proposed exploration strategy on several difficult exploration Atari 2600 games from the Arcade Learning Environment (ALE) (Bellemare et al., 2013) . We focus on 8 Atari games including FREEWAY, FROSTBITE, HERO, PRIVATEEYE, MONTEZUMA'S REVENGE, QBERT, SEAQUEST, and VENTURE. In these games, an agent without an effective exploration strategy can often converge to a suboptimal policy. For example, as depicted in Figure 2 , the Advantage Actor-Critic (A2C) baseline (Mnih et al., 2016) (Tang et al., 2017) , Sarsa-φ-EB (Martin et al., 2017) , DQN-PixelCNN (Ostrovski et al., 2017) , and Curiosity-Driven (Burda et al., 2018) . The numbers for DDQN+ were taken from (Tang et al., 2017) or were read from a plot.

• A2C: an implementation adopted from OpenAI baselines (Dhariwal et al., 2017) using the default hyperparameters, which serves as the building block of our more complicated baselines. • A2C+Pixel-SimHash: Following (Tang et al., 2017) , we map 52×52 gray-scale observations to 128-bit binary codes using random projection followed by quantization (Charikar, 2002) . Then, we add a count-based exploration bonus based on quantized observations.

As a control experiment, we evaluate A2C+CoEX+RAM * , our contingency-aware exploration method together with the ground-truth location information obtained from game's RAM. It is roughly an upper-bound of the performance of our approach.

4.2. IMPLEMENTATION DETAILS:

For the A2C (Mnih et al., 2016 ) algorithm, we use 16 parallel actors to collect the agent's experience, with 5-step rollout, which yields a minibatch of size 80 for on-policy transitions. We use the last 4 observation frames stacked as input, each of which is resized to 84 × 84 and converted to grayscale as in (Mnih et al., 2015; 2016) . We set the end of an episode to when the game ends, rather than when the agent loses a life. Each episode is initialized with a random number of no-ops (Mnih et al., 2015) .

More implementation details can be found in Appendix A and B.

For the ADM, we take observation frames of size 160×160 as input (resized from the raw observation of size 210 × 160). 5 We employ a 4-layer convolutional neural network that produces a feature map φ(s t ) with a spatial grid size of H × W = 9 × 9. As a result, the prediction of location coordinates lies in the 9 × 9 grid.

In some environments, the contingent regions within the visual observation alone are not sufficient to determine the exact location of the agent within the game; for example, the coordinate cannot solely distinguish between different rooms in HERO, MONTEZUMA'S REVENGE, and PRIVATEEYE, etc. Therefore, we introduce a discrete context representation c ∈ Z that summarizes the high-level visual context in which the agent currently lies. We use a simple clustering method similar to (Kulis & Jordan, 2012) , which we refer to as observation embedding clustering that clusters the random projection vectors of the input frames as in (Tang et al., 2017) , so that different contexts are assigned to different clusters. We further explain this heuristic approach more in detail in Appendix D.

In sparse-reward problems, the act of collecting a reward is rare but frequently instrumental for the future states of the environment. The cumulative reward R t = t-1 t =0 r ext (s t , a t ) from the beginning of the episode up to the current step t, can provide a useful high-level behavioral context because collecting rewards can trigger significant changes to the agent's state and as a result the optimal behavior can change as well. In this sense, the agent should revisit the previously visited location for exploration when the context changes. For example, in MONTEZUMA'S REVENGE, if the agent is in the first room and the cumulative reward is 0, we know the agent has not picked up the key and the optimal policy is to reach the key. However, if the cumulative reward in the first room is 100, it means the agent has picked up the key and the next optimal goal is to open a door and move on to the next room. Therefore, we could include the cumulative reward as a part of state abstraction for exploration, which leads to empirically better performance.

To sum up, for the purpose of count-based exploration, we utilize the location (x, y) of the controllable entity (i.e., the agent) in the current observation discovered by ADM (Section 3.1), a context representation c ∈ Z that denotes the high level visual context, and a cumulative environment reward R ∈ Z that represents the exploration behavioral state. In such setting, we may denote ψ(s) = (x, y, c, R).

4.3. PERFORMANCE OF COUNT-BASED EXPLORATION:

Figure 2 shows the learning curves of the proposed methods on 8 Atari games. The performance of our method A2C+CoEX and A2C+CoEX+RAM as well as the baselines A2C and A2C+Pixel-SimHash are summarized in Table 1 . In order to find a balance between the environment reward and the exploration bonus reward, we perform a hyper-parameter search for the proper weight of the environment reward β 1 and the exploration reward β 2 for A2C+CoEX+RAM, as well as for A2C+CoEX. The hyper-parameters for the two ended up being the same, which is consistent with our results. For fair comparison, we also search for the proper weight of environment reward for A2C baseline. The best hyper-parameters for each game are shown in Table 5 in Appendix B.

Compared to the vanilla A2C, the proposed exploration strategy improves the score on all the hard-exploration games. As shown in Table 1 , provided the representation (x, y, c, R) is perfect, A2C+CoEX+RAM achieves a significant improvement over A2C by encouraging the agent to visit novel locations, and could nearly solve these hard exploration games as training goes on. Furthermore, A2C+CoEX using representations learned with our proposed attentive dynamics model and observation embedding clustering also outperforms the A2C baseline. Especially on FREEWAY, FROSTBITE, HERO, MONTEZUMA'S REVENGE, QBERT and SEAQUEST, the performance is comparable with A2C+CoEX+RAM, demonstrating the usefulness of the contigency-awareness information discovered by ADM.

Comparison to other count-based exploration methods. Table 2 compares the proposed method with previous state-of-the-art results, where our proposed method outperforms the other methods on 5 out of 8 games. DQN-PixelCNN is the strongest alternative achieving a state-of-the-art performance on some of the most difficult sparse-reward games. We argue that using Q-learning as the base learner with DQN-PixelCNN makes the direct comparison with A2C+CoEX not completely adequate. Note that the closest alternative count-based exploration method to A2C+CoEX would be A3C+ (Bellemare et al., 2016) , which augments A3C (Mnih et al., 2016) with exploration bonus derived from pseudocount, because A2C and A3C share a similar policy learning method. With that in mind, one can observe a clear improvement of A2C+CoEX over A3C+ on all of the 8 Atari games.

4.4. ANALYSIS OF ATTENTIVE DYNAMICS MODEL:

We also analyze the performance of the ADM that learns the controllable dynamics of the environment. As a performance metric, we report the average distance between the ground-truth agent location (x * , y * ) and the predicted location (x, y) within the 9 × 9 grid: (x, y) -(x * , y * ) 2 . The ground-truth location of the agent is extracted from RAM6 , then rescaled so that the observation image frame fits into the 9 × 9 grid.

Figure 3 shows the results on 4 Atari games (MONTEZUMA'S REVENGE, SEAQUEST, HERO, and VENTURE). The ADM is able to quickly capture the location of the agent without any supervision of localization, despite the agent constantly visiting new places. Typically the predicted location is on average 1 or 2 grid cells away from the ground-truth location. Whenever a novel scene is encountered (e.g., the second room in MONTEZUMA'S REVENGE at around 10M steps), the average distance temporarily increases but quickly drops again as the model learns the new room. We provide videos of the agents playing and localization information as the supplementary material.7 

4.5. ANALYSIS OF OBSERVATION EMBEDDING CLUSTERING:

To make the agent aware of a change in high-level visual context (i.e., rooms in Atari games) in some games such as MONTEZUMA'S REVENGE, VENTURE, HERO, and PRIVATEEYE, we obtain a representation of the high-level context and use it for exploration. The high-level visual contexts are different from each other (different layouts, objects, colors, etc.), so the embedding generated by a random projection is quite distinguishable and the clustering is accurate and robust.

For evaluation, given an observation in Atari games, we compare the discrete representation (i.e., which cluster it is assigned to) based on the embedding from random projection to the ground-truth room number extracted from RAM. The Adjusted Rand Index (ARI) (Rand, 1971) measures the similarity between these two data clusterings. The ARI may only yield a value between 0 and 1, and is exactly 1 when the clusterings are identical.

The curves of the Adjusted Rand Index are shown in Figure 4 . For MONTEZUMA'S REVENGE and VENTURE, the discrete representation as room number is roughly as good as the ground-truth. For HERO and PRIVATEEYE, since there are many rooms quite similar to one another, it is more challenging to accurately cluster the embeddings. The samples shown in Figure 7 in Appendix D show reasonable performances of the clustering method on all these games.

4.6. ADDITIONAL EXPERIMENTS WITH PPO:

We also evaluate the proposed exploration algorithm on MONTEZUMA'S REVENGE using the sticky actions environment setup (Machado et al., 2017) identical to the setup found in (Burda et al., 2019) . In the sticky action setup, the agent randomly repeats the previous action with probability of 0.25, preventing the algorithm from simply memorizing the correct sequence of actions and relying on determinism. The agent is trained with Proximal Policy Optimization (PPO) (Schulman et al., 2017) in conjunction with the proposed exploration method using 128 parallel actors to collect the experience. We used reward normalization and advantage normalization as in (Burda et al., 2018) . The method, denoted PPO+CoEX, achieves the score of 11,618 at 500M environment steps (2 billion frames) on MONTEZUMA'S REVENGE, when averaged over 3 runs. The learning curve is illustrated in Figure 5 . Since the vanilla PPO baseline achieves a score near 0 (our runs) or 1,797 (Burda et al., 2019) , this result is not solely due to the benefits of PPO. There is another approach "Exploration by Random Network Distillation" (Burda et al., 2019) concurrent with our work which achieves similar performance by following a slightly different philosophy.

4.7. DISCUSSIONS AND FUTURE WORK:

This paper investigates whether discovering controllable dynamics via an attentive dynamics model (ADM) can help exploration in challenging sparse-reward environments. We demonstrate the effectiveness of this approach by achieving significant improvements on notoriously difficult video games. That being said, we acknowledge that our approach has certain limitations. Our currently presented instance of state abstraction method mainly focuses on controllable dynamics and employs a simple clustering scheme to abstract away uncontrollable elements of the scene. In more general setting, one can imagine using attentive (forward or inverse) dynamics models to learn an effective and compact abstraction of the controllable and uncontrollable dynamics as well, but we leave this to future work.

Key elements of the current ADM method include the use of spatial attention and modelling of the dynamics. These ideas can be generalized by a set of attention-based dynamics models (ADM) operating in forward, inverse, or combined mode. Such models could use attention over a lowerdimensional embedding that corresponds to an intrinsic manifold structure from the environment (i.e., intuitively speaking, this also corresponds to being self-aware of (e.g., locating) where the agent is in the abstract state space). Our experiments with the inverse dynamics model suggest that the mechanism does not have to be perfectly precise, allowing for some error in practice. We speculate that mapping to such subspace could be obtained by techniques of embedding learning.

We note that RL environments with different visual characteristics may require different forms of attention-based techniques and properties of the model (e.g., partial observability). Even though this paper focuses on 2D video games, we believe that the presented high-level ideas of learning contingency-awareness (with attention and dynamics models) are more general and could be applicable to more complex 3D environments with some extension. We leave this as future work.

5. CONCLUSION:

We proposed a method of providing contingency-awareness through an attentive dynamics model (ADM). It enables approximate self-localization for an RL agent in 2D environments (as a specific perspective). The agent is able to estimate its position in the space and therefore benefits from a compact and informative representation of the world. This idea combined with a variant of countbased exploration achieves strong results in various sparse-reward Atari games. Furthermore, we report state-of-the-art results of >11,000 points on the infamously challenging MONTEZUMA'S REVENGE without using expert demonstrations or supervision. Though in this work we focus mostly on 2D environments in the form of sparse-reward Atari games, we view our presented high-level concept and approach as a stepping stone towards more universal algorithms capable of similar abilities in various RL environments.

B ARCHITECTURE AND HYPERPARAMETER DETAILS:

The architecture details of the attentive dynamics model (ADM), the policy network, and hyperparameters are as follows. 

C EXPERIMENT WITH RAM INFORMATION:

In order to understand the performance of exploration with perfect representation, we extract the ground-truth location of the agent and the room number from RAM, and then run count-based exploration with the perfect (x, y, c, R). Figure 6 shows the learning curves of the experiments; we could see A2C+CoEX+RAM acts as an upper bound performance of our proposed method.

D OBSERVATION EMBEDDING CLUSTERING:

We describe the detail of a method to obtain the observation embedding. Given an observation of shape (84, 84, 3), we flatten the observation and project it to an embedding of dimension 64. We randomly initialize the parameter of the fully-connected layer for projection, and keep the values unchanged during the training to make the embedding stationary.

For the embedding of these observations, we cluster them based on a threshold value τ . The value of τ for each game with change of rooms is listed in Table 5 . If the distance between the current embedding and the center mean(c) of a cluster c is less than the threshold, we assign this embedding to the cluster with the smallest distance and update its center with the mean value of all embeddings belonging to this cluster. If the distance between the current embedding and the center of any cluster is larger than the threshold, we create a new cluster and this embedding is assigned to this new cluster. In Figure 7 , we also show the samples of observation in each cluster. We could see observations from the same room are assigned to the same cluster and different clusters correspond to different rooms. 

E ABLATION STUDY ON ATTENTIVE DYNAMICS MODEL:

We conduct a simple ablation study on the learning objectives of ADM, described in Equation ( 5). We evaluate the performance of ADM when trained on the same trajectory data under different combinations of loss terms, simulating batches of on-policy transition data to be replayed. The sample trajectory was obtained from an instance of A2C+CoEX+RAM and kept same across all the runs, which allows a fair comparison between different variants. We compare the following four methods:

• ADM (action) : train ADM using L action only • ADM (action, cell) : train ADM using L action and L cell • ADM (action, ent) : train ADM using L action and L ent • ADM (action, cell, ent) : train ADM using all losses (L action , L cell , L ent )

Figure 8 shows the average distance between the ground-truth location of the agent and the predicted one by ADM during the early stages of training. On MONTEZUMA'S REVENGE, there is only little difference between the variants although the full model worked slightly better on average. On FREEWAY, the effect of loss terms is more clear; in the beginning the agent tends to behave suboptimally by taking mostly single actions only (UP out of three action choices -UP, DOWN, and NO-OP), hence very low entropy H(π(•|s)), which can confuse the ADM of telling which part is actually controllable as the action classifier would give correct answer regardless of attention. We can observe additional loss terms help the model quickly correct the attention to localize the controllable object among the uncontrollable clutters with better stability. In another ablation study, we compare the end performance of the A2C+CoEX agent with the ADM jointly trained under different loss objectives on these three games (MONTEZUMA'S REVENGE, FREEWAY and SEAQUEST). In our experiments, the variant with full ADM worked best on MON-TEZUMA'S REVENGE and FREEWAY. The minimal training objective of ADM (i.e., L action ) also solely works reasonably well, but with the combination of other loss terms we can attain a more stable performance. 

F ABLATION STUDY ON THE STATE REPRESENTATION:

We present a result of additional ablation study on the state representation ψ(s) used in count-based exploration. The following baselines are considered:

• A2C+CoEX(c): Uses only the context embedding for exploration, i.e., ψ(s) = (c).

• A2C+CoEX(c, R): Uses only the context embedding and the cumulative reward for exploration without contingent region information, i.e., ψ(s) = (c, R). • A2C+CoEX(x, y, c): Uses the contingent region information (x, y) as well as the context embedding c, however without the cumulative reward component, i.e., ψ(s) = (x, y, c).

One can also consider another baseline similar to A2C+CoEX(c, R) with ψ(s) = (x, y, c, R), where the location information (x, y) is replaced with random coordinates uniformly sampled from the grid. It ablates the learned contingent regions. However, we found that it performs similarly to the presented A2C+CoEX(c, R) baseline.

The experimental results are summarized in Table 6 and Figure 10 . The variants without contingent regions (i.e., A2C+CoEX(c) and A2C+CoEX(c, R) performed significantly worse in most of the games than A2C+CoEX(x, y, c) and A2C+CoEX(x, y, c, R) giving little improvement over the A2C baseline. Most notably, in the games with the hardest exploration such as MONTEZUMA'S REVENGE and VENTURE, the performance is hardly better than the vanilla A2C or a random policy, achieving a score as low as zero. The variants with contingent region information worked best and comparable to each other. We observe that using the cumulative reward (total score) for exploration gives a slight improvement on some environments. These results support the effectiveness of the learned contingency-awareness information in count-based exploration.

:

We note that an entropy regularization term (e.g., Eq.(9)) is used when learning the policy.

To obtain more accurate localization by taking temporal correlation into account, we can use exponential smoothing as αt(i, j) = (1 -ωt)αt-1(i, j) + ωtαt(i, j), where ωt = max (i,j) {αt(i, j)}.

In Section 4.6, we also report experiments using Proximal Policy Optimization (PPO)(Schulman et al., 2017) as a baseline, where our PPO+CoEX achieves the average score of >11,000 on MONTEZUMA'S REVENGE.

In some games such as Venture, the agent is depicted in very small pixels, which might be hardly recognizable in rescaled 84 × 84 images.

Please note that the location from RAM is used only for analysis and evaluation purposes.

A demo video of the learnt policy and localization is available at https://coex-rl.github.io/.

Figures and Tables:

Figure 1: Left: Contingent region in FREEWAY; an object in a red box denotes what is under the agent's control, whereas the rest is not. Right: A diagram for the proposed ADM architecture.

Figure 2: Learning curves on several Atari games: A2C+CoEX and A2C. The x-axis represents total environment steps and the y-axis the mean episode reward averaged over 40 recent episodes. The mean curve is obtained by averaging over 3 random seeds, each shown in a light color.

Figure 3: Performance plot of ADM trained using on-policy samples from the A2C+CoEX agent.

Figure 4: Curves of ARI score during training of A2C+CoEX, averaged over 100 recent observations.

Figure 5: The learning curve of PPO+CoEX on several Atari games with sticky actions setup. The x-axis represents the total number of environment steps and the y-axis the mean episode reward averaged over 40 recent episodes. The mean curve is obtained by averaging over 3 random seeds, each shown in a light color.

Figure 7: Sample of clustering results for VENTURE, HERO, PRIVATEEYE, and MONTEZUMA'S REVENGE. Each column is one cluster, and we show 3 random samples assigned into this cluster.

Figure 8: Performance of ADM in terms of mean distance under different loss combinations in early stages, trained using the same online trajectory data. Plots were obtained by averaging runs over 5 random seeds.

Figure 9: Learning curves of A2C+CoEX with ADM trained under different training objectives. The curve in solid line shows the mean episode over 40 recent episodes, averaged over 3 random seeds.

Figure 10: Learning curves for the ablation study of state representation. The exploration algorithm without the contingent region information (purple) performs significantly worse, yielding almost no improvement on hard-exploration games such as MONTEZUMA'S REVENGE, VENTURE, and FROSTBITE. The mean curve is obtained by averaging over 3 random seeds. See Table6for numbers.



Table content: <table/>

achieves a reward close to 0 on MONTEZUMA'S REVENGE, VENTURE, FREEWAY, FROSTBITE, and PRIVATEEYE, even after 100M steps of training. By contrast, our proposed technique, which augments A2C with count-based exploration with the location information learned by the attentive dynamics model, denoted A2C+CoEX (CoEX stands for "Contingency-aware Exploration"), significantly outperforms the A2C baseline on six out of the 8 games. Performance of our method and its baselines on Atari games: maximum mean scores (averaged over 40 recent episodes) achieved over total 100M environment timesteps (400M frames) of training, averaged over 3 seeds. The best entry in the group of experiments without supervision is shown in bold. * denotes that A2C+CoEX+RAM acts as a control experiment, which includes some supervision. More experimental results on A2C+CoEX+RAM are shown in Appendix C.

Table content: <table><tr><td>We compare our proposed A2C+CoEX technique against the following baselines: 4</td></tr></table>



Table content: <table/>

Performance of PPO and PPO+CoEX: maximum mean scores (average over 40 recent episodes) achieved over total 500M environment steps (2B frames) of training, averaged over 3 seeds.

Table content: <table/>

Network architecture and hyperparameters

Table content: <table><tr><td>Hyperparameters</td><td>Value</td><td/></tr><tr><td>Policy and Value Network Architecture</td><td>Input: 84x84x1</td><td/></tr><tr><td/><td>-Conv(32-8x8-4)</td><td>/ReLU</td></tr><tr><td/><td>-Conv(64-4x4-2)</td><td>/ReLU</td></tr><tr><td/><td>-Conv(64-3x3-1)</td><td>/ReLU</td></tr><tr><td/><td>-FC(512)</td><td>/ReLU</td></tr><tr><td/><td>-FC(|A|), FC(1)</td><td/></tr><tr><td>ADM Encoder Architecture</td><td>Input: 160x160x3</td><td/></tr><tr><td/><td>-Conv(8-4x4-2)</td><td>/LeakyReLU</td></tr><tr><td/><td>-Conv(8-3x3-2)</td><td>/LeakyReLU</td></tr><tr><td/><td>-Conv(16-3x3-2)</td><td>/LeakyReLU</td></tr><tr><td/><td>-Conv(16-3x3-2)</td><td>/LeakyReLU</td></tr><tr><td>MLP Architecture for et(i, j)</td><td>FC(1296,256)</td><td>/ReLU</td></tr><tr><td/><td>-FC(256,128)</td><td>/ReLU</td></tr><tr><td/><td>-FC(128,|A|)</td><td/></tr><tr><td>MLP Architecture for αt(i, j)</td><td>FC(1296,64)</td><td>/ReLU</td></tr><tr><td/><td>-FC(64,64)</td><td>/ReLU</td></tr><tr><td/><td>-FC(64,1)</td><td/></tr><tr><td>λent for Loss</td><td>0.001</td><td/></tr><tr><td>A2C Discount Factor γ</td><td>0.99</td><td/></tr><tr><td>Learning Rate (RMSProp)</td><td>0.0007</td><td/></tr><tr><td>Number of Parallel Environments</td><td>16</td><td/></tr><tr><td>Number of Roll-out Steps per Iteration</td><td>5</td><td/></tr><tr><td>Entropy Regularization of Policy (α)</td><td>0.01</td><td/></tr><tr><td>PPO Discount Factor γ</td><td>0.99</td><td/></tr><tr><td>λ for GAE</td><td>0.95</td><td/></tr><tr><td>Learning rate (Adam)</td><td>0.00001</td><td/></tr><tr><td>Number of Parallel Environments</td><td>128</td><td/></tr><tr><td>Rollout Length</td><td>128</td><td/></tr><tr><td>Number of Minibatches</td><td>4</td><td/></tr><tr><td>Number of Optimization Epochs</td><td>4</td><td/></tr><tr><td colspan="2">Coefficient of Extrinsic and Intrinsic reward β1 = 2, β2 = 1</td><td/></tr><tr><td>Entropy Regularization of Policy (α)</td><td>0.01</td><td/></tr></table>

The list of hyperparameters used for A2C+CoEX in each game. For the four games where there is no change of high-level visual context (FREEWAY, FROSTBITE, QBERT and SEAQUEST), we do not include c in the state representation ψ(s), hence there is no τ . The same values of τ are used in PPO+CoEX.

Table content: <table><tr><td>Games</td><td colspan="4">β1 in A2C+CoEX β2 in A2C+CoEX β1 in A2C τ for clustering</td></tr><tr><td>FREEWAY</td><td>10</td><td>10</td><td>10</td><td>-</td></tr><tr><td>FROSTBITE</td><td>10</td><td>10</td><td>10</td><td>-</td></tr><tr><td>HERO</td><td>1</td><td>0.1</td><td>1</td><td>0.7</td></tr><tr><td colspan="2">MONTEZUMA'S REVENGE 10</td><td>10</td><td>10</td><td>0.7</td></tr><tr><td>PRIVATEEYE</td><td>10</td><td>10</td><td>10</td><td>0.55</td></tr><tr><td>QBERT</td><td>1</td><td>0.5</td><td>1</td><td>-</td></tr><tr><td>SEAQUEST</td><td>1</td><td>0.5</td><td>10</td><td>-</td></tr><tr><td>VENTURE</td><td>10</td><td>10</td><td>10</td><td>0.7</td></tr></table>

Table 6 for numbers. Summary of the results of the ablation study of the state representation. We report the maximum mean score (averaged over 40 recent episodes) achieved over 100M environment steps, averaged over 3 random seeds.

Table content: <table><tr><td>Method</td><td colspan="8">Freeway Frostbite Hero Montezuma PrivateEye Qbert Seaquest Venture</td></tr><tr><td>A2C</td><td>7.2</td><td>1099</td><td>34352</td><td>12.5</td><td>574</td><td>19620</td><td>2401</td><td>0</td></tr><tr><td>A2C+CoEX (c)</td><td>10.7</td><td>1313</td><td>34269</td><td>14.7</td><td>2692</td><td>20942</td><td>1810</td><td>94</td></tr><tr><td>A2C+CoEX (c, R)</td><td>34.0</td><td>941</td><td>34046</td><td>9.2</td><td>5458</td><td>21587</td><td>2056</td><td>77</td></tr><tr><td>A2C+CoEX (x, y, c)</td><td>33.7</td><td>5066</td><td>36934</td><td>6558</td><td>5377</td><td>21130</td><td>1978</td><td>1374</td></tr><tr><td>A2C+CoEX (x, y, c, R)</td><td>34.0</td><td>4260</td><td>36827</td><td>6635</td><td>5316</td><td>23962</td><td>5169</td><td>204</td></tr></table>

Peer review (iclr2019):
 This paper investigates the problem of extracting a meaningful state representation to help with exploration in RL, when confronted to a sparse reward task. The core idea consists in identifying controllable (learned) features of the state, which in an Atari game for instance typically corresponds to the position of the player-controlled character / vehicle on the screen. Once this position is known (as x, y coordinates on a custom low-resolution grid), one can use existing count-based exploration mechanisms to encourage the agent to visit new positions (NB: in addition to the x, y coordinates, extra information is also used to disambiguate the states for counting purpose, namely the current score and the state’s cluster index obtained with a basic clustering scheme). To find the position, the algorithm trains one inverse dynamics model per x, y cell on the grid: each model tries to predict the action taken by the agent given two consecutive states, both represented by their feature map (at coordinate x, y) learned by a convolutional network applied to the pixel representation. The outputs of these inverse dynamics models are combined through an attention mechanism to output the final prediction for the action: the intuition is that the attention model will learn to focus on the grid cell with best predictive power (for a given state), which should correspond to where the controllable parts of the state are. Experiments on several Atari games (including Montezuma’s Revenge) indeed show that this mechanism is able to track the true agent’s coordinates (obtained from the RAM state) reasonably well. Using these coordinates for count-based exploration (in A2C) also yields significantly better results compared to vanilla A2C, and beats several previously proposed related techniques for exploration in sparse reward settings. The topic being investigated here (hard-exploration tasks) is definitely very relevant to current RL research, and the proposed technique introduces some novel ideas to address it, notably the usage of an attention model combined with multiple inverse dynamics models so as to identify controllable features in the environment. The approach seems sound to me and is clearly explained. Combined with pretty good results on well known hard Atari games, I am leaning toward recommending acceptance at ICLR. I have a few significant concerns though, the first one being that the end result seems quite tailored to the specific Atari games of interest: trying to apply it to other tasks (or even just Atari games with different characteristics) may require significant changes (ex: the assumption that a single region of the screen is being controlled by the agent, the clustering to identify the various “rooms” of a game, and using the total score as a proxy to important state information). I do believe that some components are more general though (in particular the main new ideas in the paper), so this is not necessarily a major issue, but another example of application of these ideas to a different domain could have strengthened the submission. In addition, even if experiments definitely investigate relevant aspects of the algorithm, I wish there had been an ablation study on the three components of the state representation used for counting (coordinates, cluster and reward). In particular it would be disappointing if similar results could be obtained with just the cluster and reward... even if I do not expect it to be the case, an empirical validation would have been welcome to be 100% sure. The good results obtained here from exploration alone also beg the question whether this state representation could be useful to train the agent, by plugging it directly as input to the policy network (which by the way may not be trivial due to the co-training, but you get the idea). I realize that the focus of the paper is on exploration, and this is fine, but it seems to me a bit of a waste to build such a powerful state abstraction mechanism and not give the agent access to it. I was surprised that it was not at least mentioned in the discussion or conclusion. Note by the way that the conclusion says the agent “benefits from a compact, informative representation of the world”, which can be misinterpreted as using it in its policy. Regarding the algorithm itself, one potential limitation is the fact that the inverse dynamics models rely on a single time step to identify the action that was taken. This means that they can only identify controllable state features that change immediately after taking a given action. But if an action has “cascading” effects (the immediate state change causing further changes down the road), there may be other important state features that could be controlled (across longer timesteps), but the algorithm will ignore them (also, in a POMDP one may need to wait for more than one timestep to even observe a single change in the state). I suspect that a more generic variant of this idea, better accounting for long term effects of actions, may thus be needed in order to work optimally in more varied settings. Finally, I believe more papers deserve to be cited in the “Related Work” section. In particular, the idea of controlling features of the environment, (even if not specifically for exploration), has also been explored in (at least) the following papers: - “Reinforcement Learning with Unsupervised Auxiliary tasks” (Jaderberg et al, 2017) - “Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning” (Dilokthanakul et al, 2017) - “Independently Controllable Factors” (Thomas et al, 2017) - “Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World” (Sawada, 2018) Relying on the position of the agent on the screen to drive exploration in Atari games has also been used in: “Deep Curiosity Search: Intra-Life Exploration Improves Performance on Challenging Deep Reinforcement Learning Problems” (Stanton & Clune, 2018) Other remarks: - Please share the code if possible - In the Introduction, the sentence “it is still an open question on how to construct an optimal representation for exploration” seems to repeat “there is an ongoing open question about the most effective way of using neural network representations for exploration” => I wonder if one was supposed to replace the other? - On p.2, last line containing citations: Pathak et al should be in the parentheses - Please explicitly refer to Fig. 1 (Right) in 3.1 - On p.4, three lines above eq. 5, there is a hat{alpha} that should probably be hat{a} - Is the left hand side L in eq. 5 the same as L^inv in Alg. 1? If so please use the same notations - “privious” work in 3.2 - In 3.2 please briefly explain what psi is going to be. It is a bit confusing to have it appear “out of nowhere“, with no details on how it is constructed. - Please explain what the different shades mean in Fig. 2-3 - In Table 2’s caption please add a reference for DQN-PixelCNN. Also what do the star and cross symbols mean next to the algorithms’ names? - “coule” at end of 4.6 - The “Watson” citation is duplicated in references - Why are there games with no tau in Table 4? Is it because there was no such clustering on these games? (if yes, that was not clear in the paper). And how was tau chosen for other games? (in particular I want to make sure the RAM state was not used to optimize it) Update 2018-11-23: I am reducing my rating to 5 (from 6) due to the absence of author response regarding a potential revision addressing my comments/questions as well as those from other reviewers Update 2018-11-27: I am increasing my rating to 7 (from 5) after the authors responded to reviewers* comments and uploaded a revised version of the paper