Efficient Exploration via State Marginal Matching
To solve tasks with sparse rewards, reinforcement learning algorithms must be equipped with suitable exploration techniques. However, it is unclear what underlying objective is being optimized by existing exploration algorithms, or how they can be altered to incorporate prior knowledge about the task. Most importantly, it is difficult to use exploration experience from one task to acquire exploration strategies for another task. We address these shortcomings by learning a single exploration policy that can quickly solve a suite of downstream tasks in a multi-task setting, amortizing the cost of learning to explore. We recast exploration as a problem of State Marginal Matching (SMM): we learn a mixture of policies for which the state marginal distribution matches a given target state distribution, which can incorporate prior knowledge about the task. Without any prior knowledge, the SMM objective reduces to maximizing the marginal state entropy. We optimize the objective by reducing it to a two-player, zero-sum game, where we iteratively fit a state density model and then update the policy to visit states with low density under this model. While many previous algorithms for exploration employ a similar procedure, they omit a crucial historical averaging step, without which the iterative procedure does not converge to a Nash equilibria. To parallelize exploration, we extend our algorithm to use mixtures of policies, wherein we discover connections between SMM and previously-proposed skill learning methods based on mutual information. On complex navigation and manipulation tasks, we demonstrate that our algorithm explores faster and adapts more quickly to new tasks.111Videos and code: https://sites.google.com/view/state-marginal-matching
Efficient Exploration via State Marginal Matching
Lisa Lee Carnegie Mellon University email@example.com Benjamin Eysenbach CMU, Google Brain firstname.lastname@example.org Emilio Parisotto Carnegie Mellon University email@example.com Eric Xing Carnegie Mellon University Sergey Levine UC Berkeley, Google Brain Ruslan Salakhutdinov Carnegie Mellon University
In order to solve tasks with sparse or delayed rewards, reinforcement learning (RL) algorithms must be equipped with suitable exploration techniques. Exploration methods based on random actions have limited ability to cover a wide range of states. More sophisticated techniques, such as intrinsic motivation, can be much more effective. However, it is often unclear what underlying objective is optimized by these methods, or how prior knowledge can be readily incorporated into the exploration strategy. Most importantly, it is difficult to use exploration experience from one task to acquire exploration strategies for another task.
We address these shortcomings by considering a multi-task setting, where many different reward functions can be provided for the same set of states and dynamics. Rather than re-inventing the wheel and learning to explore anew for each task, we aim to learn a single, task-agnostic exploration policy that can be adapted to many possible downstream reward functions, amortizing the cost of learning to explore. This exploration policy can be viewed as a prior on the policy for solving downstream tasks. Learning will consist of two phases: during training, we acquire this task-agnostic exploration policy; during testing, we use this exploration policy to quickly explore and maximize the task reward.
Learning a single exploration policy is considerably more difficult than doing exploration throughout the course of learning a single task. The latter is done by intrinsic motivation (Pathak et al., 2017; Tang et al., 2017; Oudeyer et al., 2007) and count-based exploration methods (Bellemare et al., 2016), which can effectively explore to find states with high reward, at which point the agent can decrease exploration and increase exploitation of those high-reward states. While these methods perform efficient exploration for learning a single task, the policy at any particular iteration is not a good exploration policy. For example, the final policy at convergence would only visit the high-reward states discovered for the current task. A straightforward solution is to simply take the historical average over policies from each iteration of training. At test time, we can sample one of the historical policies from a previous training iteration, and use the corresponding policy to sample actions in that episode. Our algorithm will implicitly do this.
What objective should be optimized during training to obtain a good exploration policy? We recast exploration as a problem of State Marginal Matching: given a desired state distribution, we learn a mixture of policies for which the state marginal distribution matches this desired distribution. Without any prior information, this objective reduces to maximizing the marginal state entropy , which encourages the policy to visit as many states as possible. The distribution matching objective also provides a convenient mechanism for humans to incorporate prior knowledge about the task, whether in the form of constraints that the agent should obey; preferences for some states over other states; reward shaping; or the relative importance of each state dimension for a particular task.
We propose an algorithm to optimize the State Marginal Matching (SMM) objective. First, we reduce the problem of SMM to a two-player, zero-sum game between a policy player and a density player. We find a Nash Equilibrium for this game using fictitious play (Brown, 1951), a classic procedure from game theory. Our resulting algorithm iteratively fits a state density model and then updates the policy to visit states with low density under this model. While many previous algorithms for exploration employ a similar procedure, they omit a crucial historical averaging step, without which the iterative procedure is not guaranteed to converge.
In short, our paper studies the State Marginal Matching objective as a principled objective for acquiring a task-agnostic exploration policy. We propose an algorithm to optimize this objective. Our analysis of this algorithm sheds light on prior methods, and we empirically show that SMM solves hard exploration tasks faster than state-of-the-art baselines in navigation and manipulation domains.
2 Related Work
Most prior work on exploration has looked at exploration bonuses and intrinsic motivation. Typically, these algorithms (Pathak et al., 2017; Oudeyer et al., 2007; Schmidhuber, 1991; Houthooft et al., 2016; Burda et al., 2018) formulate some auxiliary task, and use prediction error on that task as an exploration bonus. Another class of methods (Tang et al., 2017; Bellemare et al., 2016; Schmidhuber, 2010) directly encourage the agent to visit novel states. While all methods effectively explore during the course of solving a single task, the policy obtained at convergence is often not a good exploration policy. For example, consider an exploration bonus derived from prediction error of an inverse model (Pathak et al., 2017). At convergence, the inverse model will have high error at states with stochastic dynamics, so the resulting policy will always move towards these stochastic states and fail to explore the rest of the state space.
Many exploration algorithms can be classified by whether they do exploration in the space of actions, policy parameters, goals, or states. Common exploration strategies including -greedy and Ornstein–Uhlenbeck noise (Lillicrap et al., 2015), as well as standard MaxEnt algorithms (Ziebart, 2010; Haarnoja et al., 2018), do exploration in action space. Recent work (Fortunato et al., 2017; Plappert et al., 2017) shows that adding noise to the parameters of the policy can result in good exploration. Most closely related to our work are methods (Pong et al., 2019; Hazan et al., 2018) that perform exploration in the space of states or goals. In fact, Hazan et al. (2018), consider the same State Marginal Matching objective that we examine. However, the algorithm proposed there requires an oracle planner and an oracle density model, assumptions that our method will not require. Finally, some prior work considers exploration in the space of goals (Colas et al., 2018; Held et al., 2017; Nair et al., 2018; Pong et al., 2019). In Appendix D.3, we also discuss how goal-conditioned RL (Kaelbling, 1993; Schaul et al., 2015) can be viewed as a special case of State Marginal Matching when the goal-sampling distribution is learned jointly with the policy.
The problems of exploration and meta-reinforcement learning are tightly coupled. Exploration algorithms visit a wide range of states with the aim of finding new states with high reward. Meta-reinforcement learning algorithms (Duan et al., 2016; Finn et al., 2017; Rakelly et al., 2019; Mishra et al., 2017) must perform effective exploration if they hope to solve a downstream task. Some prior work has explicitly looked at the problem of learning to explore (Gupta et al., 2018; Xu et al., 2018). However, these methods rely on meta-learning algorithms which are often complicated and brittle. 11todo: 1Mention task-agnostic exploration such as MAESN, which aims to learn a general exploration policy for any MDP (which is probably the uniform density over states). Whereas our goal is exploring new tasks from a class of MDPs we’ve encountered before.: We discuss MAESN above. I don’t think that MAESN would work on unseen MDPs
Closely related to our approach is standard maximum action entropy algorithms (Haarnoja et al., 2018; Kappen et al., 2012; Rawlik et al., 2013; Ziebart et al., 2008; Theodorou and Todorov, 2012). While these algorithms are referred to as MaxEnt RL, they are maximizing entropy over actions, not states. This class of algorithms can be viewed as performing inference on a graphical model where the likelihood of a trajectory is given by its exponentiated reward (Toussaint and Storkey, 2006; Levine, 2018; Abdolmaleki et al., 2018). While distributions over trajectories define distributions over states, the relationship is complicated. Given a target distribution over states, it is quite challenging to design a reward function such that the optimal maximum action entropy policy matches the target state distribution. Our Algorithm 1 avoids learning the reward function and instead directly learns a policy that matches the target distribution.
Finally, the idea of distribution matching has been employed successfully in imitation learning settings (Ziebart et al., 2008; Ho and Ermon, 2016; Finn et al., 2016; Fu et al., 2017). While inverse RL algorithms assume access to expert trajectories, we instead assume access to the density of the target state marginal distribution. Similar to inverse RL algorithms (Ho and Ermon, 2016; Fu et al., 2018), our method can likewise be interpreted as learning a reward function, though our reward function is obtained via a density model instead of a discriminator. 22todo: 2Mention that it is often easier to specify a target state marginal distribution rather than provide expert demonstrations?
3 State Marginal Matching
In this section, we propose the State Marginal Matching problem as a principled objective for learning to explore, and offer an algorithm for optimizing it. We consider a parametric policy that chooses actions in a Markov Decision Process (MDP) with fixed episode lengths , dynamics distribution , and initial state distribution . The MDP together with the policy form an implicit generative model over states. We define the state marginal distribution as the probability that the policy visits state :
We emphasize that is not a distribution over trajectories, and is not the stationary distribution of the policy after infinitely many steps, but rather the distribution over states visited in a finite-length episode.222 approaches the policy’s stationary distribution in the limit as the episodic horizon . We also note that any trajectory distribution matching problem can be reduced to a state marginal matching problem by augmenting the current state to include all previous states.
We assume that we are given a target distribution that encodes our uncertainty about the tasks we may be given at test-time. For example, a roboticist might assign small values of to states that are dangerous, regardless of the desired task. Alternatively, we might also learn from data about human preferences (Christiano et al., 2017). For goal-reaching tasks, we can analytically derive the optimal target distribution (Appendix C). Given , our goal is to find a parametric policy that is “closest” to this target distribution, where we measure discrepancy using the Kullback-Leibler divergence:
Note that we use the reverse-KL (Bishop, 2006), which is mode-covering (i.e., exploratory). We show in Appendix C that the policies obtained via State Marginal Matching provide an optimal exploration strategy for a particular distribution over reward functions. To gain intuition for the State Marginal Matching objective, we decomposed it in two ways. In Equation 3, we see that State Marginal Matching is equivalent to maximizing the reward function while simultaneously maximizing the entropy of states. Note that, unlike traditional MaxEnt RL algorithms (Ziebart et al., 2008; Haarnoja et al., 2018), we regularize the entropy of the state distribution, not the conditional distribution of actions given states, which results in exploration in the space of states rather than in actions. Moreover, Equation 2 suggests that State Marginal Matching maximizes a pseudo-reward , which assigns positive utility to states that the agent visits too infrequently and negative utility to states visited too frequently (see Figure 1). We emphasize that maximizing this pseudo-reward is not a RL problem because the pseudo-reward depends on the policy.
3.1 Optimizing the State Marginal Matching Objective
Optimizing Equation 2 to obtain a single exploration policy is more challenging than standard RL because the reward function itself depends on the policy. To break this cyclic dependency, we introduce a parametric state density model to approximate the policy’s state marginal distribution, . We assume that the class of density models is sufficiently expressive to represent every policy:
For every policy , there exists such that .
Now, we can optimize the policy w.r.t. the proxy distribution. Let policies and density models satisfying Assumption 1 be given. For any target distribution , the following optimization problems are equivalent:
To see this, note that
By Assumption 1, for some , so we obtain the desired result:
Solving the new max-min optimization problem is equivalent to finding the Nash equilibrium of a two-player, zero-sum game: a policy player chooses the policy while the density player chooses the density model . To avoid confusion, we use actions to refer to controls output by the policy in the traditional RL problem and strategies to refer to the decisions of the policy player and decisions of the density player. The Nash existence theorem (Nash, 1951) proves that such a stationary point always exists for such a two-player, zero-sum game.
One common approach to saddle point games is to alternate between updating player A w.r.t. player B, and updating player B w.r.t. player A. However, simple games such as Rock-Paper-Scissors illustrate that such a greedy approach is not guaranteed to converge to a stationary point. A slight variant, fictitious play (Brown, 1951) does converge to a Nash equilibrium in finite time Robinson (1951); Daskalakis and Pan (2014). At each iteration, each player chooses their best strategy in response to the historical average of the opponent’s strategies. In our setting, fictitious play alternates between (1) fitting the density model to the historical average of policies, and (2) updating the policy with RL to minimize the log-density of the state, using a historical average of the density models:
We summarize the resulting algorithm in Algorithm 1. In practice, we can efficiently implement Equation 6 and avoid storing the policy parameters from every iteration by instead storing sampled states from each iteration.333One way is to maintain an infinite-sized replay buffer, and fit the density to the replay buffer at each iteration. Alternatively, we can replace older samples in a fixed-size replay buffer less frequently such that sampling from is uniform over iterations. We cannot perform the same trick for Equation 7, and instead resort to approximating the historical average of density models with the most recent iterate
3.2 Do Exploration Bonuses Using Predictive Error Perform State Marginal Matching?
where is our autoencoder and is the KL penalty on the VAE encoder for the data distribution . In contrast, the objective for RND (Burda et al., 2018) is
where is an encoder obtained by a randomly initialized neural network. Exploration bonuses based on the predictive error of forward models (Schmidhuber, 1991; Chentanez et al., 2005; Stadie et al., 2015) have a similar form, but instead consider full transitions:
Exploration bonuses derived from inverse models (Pathak et al., 2017) look similar:
Each of these methods can be interpreted as almost learning a particular density model of and using the log-probability under that density model as a reward. However, because they omit the historical averaging step, they do not actually perform distribution matching. This provides an interesting interpretation of state marginal matching as a more principled way to apply intrinsic motivation: instead of simply taking the latest policy, which is not by itself optimizing any particular objective, we take the historical average, which can be shown to match the target distribution asymptotically.
3.3 Better Marginal Matching with Mixture of Policies
Given the challenging problem of exploration in large state spaces, it is natural to wonder whether we can accelerate exploration by automatically decomposing the potentially-multimodal target distribution into a mixture of “easier-to-learn” distributions and learn a corresponding set of policies to do distribution matching for each component. Note that the mixture model we introduce here is orthogonal to the historical averaging step discussed before. Using to denote the state distribution of the policy conditioned on the latent variable , the state marginal distribution of the mixture of policies is
where is a latent prior. As before, we will minimize the KL divergence between this mixture distribution and the target distribution. Using Bayes’ rule to re-write in terms of conditional probabilities, we obtain the following optimization problem:
Intuitively, this says that the agent should go to states (a) with high density under the target state distribution, (b) where this agent has not been before, and (c) where this agent is clearly distinguishable from the other agents. The last term (d) says to explore in the space of mixture components . This decomposition bears a resemblance to the mutual-information objectives in recent work (Achiam et al., 2018; Eysenbach et al., 2018; Co-Reyes et al., 2018). Thus, one interpretation of our work is as explaining that mutual information objectives almost perform distribution matching. The caveat is that prior work omits the state entropy term which provides high reward for visiting novel states, possibly explaining why these previous works have failed to scale to complex tasks.
We summarize the resulting procedure in Algorithm 2. The only difference from before is that we learn a discriminator , in addition to updating the density models and the policies . Jensen’s inequality tells us that maximizing the log-density of the learned discriminator will maximize a lower bound on the true density (see Agakov (2004)):
In our experiments, we leave the latent prior as fixed and uniform. Note that the updates for each can be conducted in parallel. A distributed implementation would emulate broadcast-collect algorithms (Lynch, 1996), with each worker updating the policy independently, and periodically aggregating results to update the discriminator .
In this section, we empirically study whether our method learns to explore effectively, and compare against prior exploration methods. Our experiments will demonstrate how State Marginal Matching provides good exploration, a key component of which is the historical averaging step. More experimental details can be found in Appendix E.1, and code will be released upon publication.
Baselines: We compare to a state-of-the-art off-policy MaxEnt RL algorithm, Soft Actor-Critic (SAC) (Haarnoja et al., 2018) and three exploration methods: Count-based Exploration (C), which discretizes states and uses as an exploration bonus; Pseudo-counts (Bellemare et al., 2016) (PC), which obtains an exploration bonus from the recoding probability; and Intrinsic Curiosity Module (ICM) (Pathak et al., 2017), which uses prediction error as an exploration bonus.
Manipulation Task: The manipulation environment (Plappert et al., 2018) (shown on the right) consists of a robot with a single gripper arm, and a block object resting on top of a table surface, with a 33todo: 3check this10-dimensional observation space and a 4-dimensional action space. The robot’s task is to move the object to a goal location which is not observed by the robot, thus requiring the robot to explore by moving the block to different locations on the table. At the beginning of each episode, we spawn the object at the center of the table, and the robot gripper above the initial block position. We terminate the episode after 50 environment steps, or if the block falls off the table.
Navigation Task: The agent is spawned at the center of long hallways that extend radially outward, like the spokes of a wheel, as shown on the right. The agent’s task is to navigate to the end of a goal corridor. We can vary the length of the hallway and the number of halls to finely control the task difficulty and measure how well various algorithms scale as the exploration problem becomes more challenging. We consider two types of robots: in 2D Navigation, the agent is a point mass whose position is directly controlled by velocity actions 44todo: 4check this; in 3D Navigation, the agent is the quadrupedal robot from Schulman et al. (2015), which has a 55todo: 5check this113-dimensional observation space and a 7-dimensional action space.
Implementation Details. The extrinsic environment reward implicitly defines the target distribution: . We use a VAE to model the density for both SMM and Pseudocounts (PC). For SMM, we use discrete latent skills . All results are averaged over 4 random seeds.
4.1 Experimental Results
Question 1: Is exploration more effective with maximizing state entropy or action entropy?
MaxEnt RL algorithms such as SAC maximize entropy over actions, which is often motivated as leading to good exploration. In contrast, the State Marginal Matching objective leads to maximizing entropy over states. In this experiment, we compared our method to SAC on the navigation task. To see how each method scaled, we also increased the number of hallways (# Arms) to increase the exploration challenge. To evaluate each method, we counted the number of hallways that the agent fully explored (i.e., reached the end) during training. Figure 2 shows that our method, which maximizes entropy over states, consistently explores 60% of hallways, whereas MaxEnt RL, which maximizes entropy over actions, rarely visits more than 20% of hallways. Further, using mixtures of policies (§ 3.3) explores even better.444In all experiments, we run each method for the same number of environment transitions; a mixture of 3 policies does not get to take 3 times more transitions. Figure 2 also shows the state visitations for the three hallway environment, illustrating that SAC only explores one hallway whereas SMM explores all three.
Question 2: Does historical averaging improve exploration?
While historical averaging is necessary to guarantee convergence (§ 3.1), most prior exploration methods do not employ historical averaging, raising the question of whether it is necessary in practice. To answer this question, we compare SMM to three exploration methods. For each method, we compare (1) the policy obtained at convergence with (2) the historical average of policy iterates over training. We measure how well each explores by computing the marginal state entropy, which we compute by discretizing the state space.555Discretization is used only for evaluation, no policy has access to it (except for Count). Figure 2(a) shows that historical averaging improves exploration of SMM, and can even improve exploration of the baselines w.r.t. the gripper position.
Question 3: Does State Marginal Matching allow us to quickly find unknown goals?
In this experiment, we evaluate whether the exploration policy acquired by our method efficiently explores to solve a wide range of downstream tasks. On the manipulation environment, we defined the target distribution to be uniform over the entire state space (joint + block configuration), with the constraint that we put low probability mass on states where the block has fallen off the table. The target distribution also incorporated the prior that actions should be small and the arm should be close to the object. As shown in Figure 2(c), our method has learned to explore better than the baselines, finding over 80% of the goals. Figure 9 illustrates which goals each method succeeded in finding. Our method succeeds in finding a wide range of goals.
Question 4: Can injecting prior knowledge via the target distribution bias exploration?
One of the benefits of the State Marginal Matching objective is that is allows users to easily incorporate prior knowledge about the task. In this experiment, we check whether prior knowledge injected via the target distribution is reflected in the policy obtained from State Marginal Matching. Using the same manipulation environment as above, we modified the target distribution to assign larger probability to states where the block was on the left half of the table than on the right half. In Figure 4, we plot the state marginals of the block Y-coordinate (where is left half of the table). We see that our method acquires a policy whose state distribution closely matches the target distribution.
In this paper, we introduced a formal objective for exploration. While it is often unclear what existing exploration algorithms will converge to, our State Marginal Matching objective has a clear solution: at convergence, the policy should visit states in proportion to their density under a target distribution. Not only does this objective encourage exploration, it also provides human users with a flexible mechanism to bias exploration towards states they prefer and away from dangerous states. Upon convergence, the resulting policy can thereafter be used as a prior in a multi-task setting, amortizing exploration and enabling faster adaptation to new, potentially sparse, reward functions. The algorithm we proposed looks quite similar to previous exploration methods based on prediction error, suggesting that those methods are also performing some sort of distribution matching. However, by deriving our method from first principles, we note that these prior methods omit a crucial historical averaging step, without which the algorithm is not guaranteed to converge. Experiments on navigation and manipulation tasks demonstrated how our method learns to explore, enabling an agent to efficiently explore in new tasks provided at test time.
In future work, we aim to study connections between inverse RL, MaxEnt RL and state marginal matching, all of which perform some sort of distribution matching. Empirically, we aim to scale to more complex tasks by parallelizing the training of all mixture components simultaneously. Broadly, we expect the state distribution matching problem formulation to enable the development of more effective and principled RL methods that reason about distributions rather than individual states.
We would like to thank Maruan Al-Shedivat for helpful discussions and comments. LL is supported by NSF grant DGE-1745016 and AFRL contract FA8702-15-D-0002. BE is supported by Google. EP is supported by ONR grant N000141812861 and Apple. RS is supported by NSF grant IIS1763562, ONR grant N000141812861, AFRL CogDeCON, and Apple. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF, AFRL, ONR, Google or Apple. We also thank Nvidia for their GPU support.
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Appendix A Graphical Models for State Marginal Matching
In Figure 4(a) we show that the State Marginal Matching objective can be viewed as a projection of the target distribution onto the set of realizable policies. Figures 4(b) and 4(c) illustrate the generative models for generating states in the single policy and mixture policy cases.
Appendix B A Simple Experiment
We consider a simple task to illustrate why action entropy is insufficient for distribution matching. We consider an MDP with two states and two actions, shown in Figure 6, and no reward function. A standard maximum action-entropy policy (e.g., SAC) will choose actions uniformly at random. However, because the self-loop in state A has a smaller probability that the self-loop in state B, the agent will spend 60% of its time in state B and only 40% of its time in state A. Thus, maximum action entropy policies will not yield uniform state distributions. We apply our method to learn a policy that maximizes state entropy. As shown in Figure 6, our method achieves the highest possible state entropy.
While we consider the case without a reward function, we can further show that there does not exist reward function for which the optimal policy achieves a uniform state distribution. It is enough to consider the relative reward on state A and B. If , then the agent will take actions to remain at state A as often as possible; the optimal policies achieves remains at state A 91% of the time. If , the optimal policy can remain at state B 100% of the time. if , all policies are optimal and we have no guarantee that an arbitrary policy will have a uniform state distribution.
Appendix C Choosing for Goal-Reaching Tasks
In general, the choice of the target distribution will depend on the distribution of test-time tasks. In this section, we consider the special case where the test-time tasks correspond to goal-reaching derive the optimal target distribution .
We consider the setting where goals are sampled from some known distribution. Our goal is to minimize the number of episodes required to reach that goal state. We define reaching the goal state as visiting a state that lies within an ball of the goal, where both and the distance metric are known.
We start with a simple lemma that shows that the probability that we reach the goal at any state in a trajectory is at least the probability that we reach the goal at a randomly chosen state in that same trajectory. Defining the binary random variable as the event that the state at time reaches the goal state, we can formally state the claim as follows:
We start by noting the following implication:
Thus, the probability of the event on the RHS must be at least as large as the probability of the event on the LHS:
Next, we look at the expected number of episodes to reach the goal state. Since each episode is independent, the expected hitting time is simply
Note that we have upper-bounded the hitting time using Lemma C.1. Since the goal is a random variable, we take an expectation over :
We can rewrite the RHS using to denote the target state marginal distribution:
We will minimize , an upper bound on the expected hitting time.
The state marginal distribution minimizes , where is a smoothed version of the target density.
Before diving into the proof, we provide a bit on intuition. In the case where , the optimal target distribution is . For non-zero , the policy in Lemma C.2 is equivalent to convolving with a box filter before taking the square root. In both cases, we see that the optimal policy does distribution matching to some function of the goal distribution. Note that may not sum to one and therefore is not a proper probability distribution.
We start by forming the Lagrangian:
The first derivative is
Note that the second derivative is positive, indicating that this Lagrangian is convex, so all stationary points must be global minima:
Setting the first derivative equal to zero and rearranging terms, we obtain
Swapping , we obtain the desired result. ∎
Appendix D State Marginal Matching with Mixtures of Policies
d.1 Alternative Derivation via Information Theory
The language of information theory gives an alternate view on the mixture model objective in Equation 9. First, we recall that mutual information can be decomposed in two ways:
Thus, we have the following identity:
d.2 Test-time Adaptation via Latent Posterior Update
After acquiring our task-agnostic policy during training, at test-time we want the policy to adapt to solve the test-time task. The goal of fast-adaptation belongs to the realm of meta-RL, for which prior work has proposed many algorithms (Duan et al., 2016; Finn et al., 2017; Rakelly et al., 2019). In our setting, we propose a lightweight meta-learning procedure that exploits the fact that we use a mixture of policies. Rather than adapting all parameters of our policy, we only adapt the frequency with which we sample each mixture component, which we can do simply via posterior sampling.
For simplicity, we consider test-time tasks that give sparse rewards. For each mixture component , we model the probability that the agent obtains the sparse reward: . At the start of each episode, we sample the mixture component with probability proportional to the posterior probability if obtains the reward:
Intuitively, this procedure biases us to sampling skills that previously yielded high reward. Because posterior sampling over mixture components is a bandit problem, this approach is optimal (Agrawal and Jia, 2017) in the regime where we only adapt the mixture components. We use this procedure to quickly adapt to test-time tasks in Figures 2(b) and 2(c).
d.3 Connections to Goal-Conditioned RL
Goal-Conditioned RL (Kaelbling, 1993; Nair et al., 2018; Held et al., 2017) can be viewed as a special case of State Marginal Matching when the goal-sampling distribution is learned jointly with the policy. In particular, consider the State Marginal Matching with a mixture policy (Algorithm 2), where the mixture component maps bijectively to goal states . In this case, we learn goal-conditioned policies of the form . We start by swapping for in the SMM objective with Mixtures of Policies (Equation 9):
The second term is an estimate of which goal the agent is trying to reach, similar to objectives in intent inference (Ziebart et al., 2009; Xie et al., 2013). The third term is the distribution over states visited by the policy when attempting to reach goal . For an optimal goal-conditioned policy in an infinite-horizon MDP, both of these terms are Dirac functions:
In this setting, the State Marginal Matching objective simply says to sample goals with probability equal to the density of that goal under the target distribution.
Whether goal-conditioned RL is the preferable way to do distribution matching depends on (1) the difficulty of sampling goals and (2) the supervision that will be provided at test time. It is natural to use goal-conditioned RL in settings where it is easy to sample goals, such as when the space of goals is small and finite or otherwise low-dimensional. If a large collection of goals is available apriori, we could use importance sampling to generate goals to train the goal-conditioned policy (Pong et al., 2019). However, in many real-world settings, goals are high-dimension observations (e.g., images, lidar) which are challenging to sample. While goal-conditioned RL is likely the right approach when, at test-time, we will be given a test-time task, a latent-conditioned policy make explore better in settings where the goal-state is not provided at test-time.
Appendix E Additional Experiments & Experimental Details
e.1 Environment Details
Manipulation. We use the simulated Fetch Robotics arm666https://fetchrobotics.com/ implemented by Plappert et al. (2018). The state vector includes the action taken by the robot, and xyz-coordinates of the block and the robot gripper respectively. In Manipulation-Uniform, the target state marginal distribution is given by
where are fixed weights, and the rewards
correspond to (1) a uniform distribution of the block position over the table surface (the agent receives +0 reward while the block is on the table), (2) an indicator reward for moving the robot gripper close to the block, and (3) action penalty, respectively. In Manipulation-Half, is replaced by a reward function that gives a slightly higher reward +0.1 for states where the block is on the right-side of the table. During training, all policies are trained on a weighted sum of the three reward terms: . At test-time for Manipulation-Uniform, we sample a goal block location uniformly across the table, and record the number of episodes until the agent finds the goal.
Navigation: Episodes have a maximum time horizon of 100 steps and 500 steps for 2D and 3D navigation, respectively. The environment reward is
where is the xy-position of the agent. Except in Figure 2 where we vary the number of halls, we use 3 halls for all 2D and 3D Navigation experiments. In Figure 2 and 9(a), we use a uniform target distribution over the end of all halls, so the environment reward at training time is if the robot is close enough to the end of any of the halls. In Figure 9(b) (3D Navigation), a goal is sampled in one of the three halls, and the agent must explore to find the goal.
|Domain||Env||Env Reward (||Parameters||Figure|
|Manipulation||10||4||50||Uniform||Uniform block pos. over table surface||3, 10|
|Half||More block pos. density on left-half of table||4|
|Navigation||2||2||100||2D||Uniform over all N halls||
|Uniform over all N halls||
|113||7||500||3D||One (unobserved) goal hallway||
e.2 Visualizing the Manipulation Environment
We visualize the log state marginal over block XY-coordinates in Figures 7 and 8. In Figure 9, we plot goals sampled at test-time, colored by the number of episodes each method required to push the block to that goal location.. Blue dots indicate that the agent found the goal quickly. We observe that SMM has the most blue dots, indicating that it succeeds in exploring a wide range of states at test-time.
e.3 Additional Experimental Results
To understand the relative contribution of each component in the mixture-case SMM objective (Equation 9), we compare our method to baselines that lack conditional state entropy , latent conditional action entropy , or both (i.e, SAC). We evaluate on 2D Navigation (Figure 9(a)) and 3D Navigation (Figure 9(b)). Results show that our method relies heavily on both key differences from SAC.
We show training curves for Manipulation-Half in Figure 11.
e.4 Experiment Details
Hyperparameter settings are summarized in Table 2. All algorithms were trained for 1K epochs (1M env steps) for Manipulation and 3D Navigation, and 100 epochs (100K env steps) for 2D Navigation.
Loss Hyperparameters. SAC reward scale controls the action entropy reward w.r.t. the extrinsic reward. Count coeff controls the intrinsic count-based exploration reward w.r.t. the extrinsic reward and SAC action entropy reward. Similarly, Pseudocount coeff controls the intrinsic pseudocount exploration reward. SMM coeff for and control the different loss components (state entropy and latent conditional entropy) of the SMM objective in Equation 9.
Historical Averaging. In Manip. experiments, we tried the following sampling strategies for historical averaging: (1) Uniform: Sample policies uniformly across training iterations. (2) Exponential: Sample policies, with recent policies sampled exponentially more than earlier ones. (3) Last: Sample the latest policies uniformly at random. We found that Uniform worked less well, possibly due to the policies at early iterations not being trained enough. We found negligible difference in the state entropy metric between Exponential vs. Last, and between sampling 5 vs. 10 historical policies. Note that since we only sample 10 checkpoints, it is unnecessary to keep checkpoints from every iteration.
Network Hyperparameters. For all algorithms, we use a Gaussian policy with two hidden layers of size (300, 300) with Tanh activation and a final fully-connected layer. The Value function and Q-function each are a feedforward MLP with two hidden layers of size (300, 300) with ReLU activation and a final fully-connected layer. The same network configuration is used for the SMM discriminator but with different input and output sizes. The SMM density model is modeled by a VAE with encoder and decoder networks each consisting of two hidden layers of size (150, 150) with ReLU activation. The same VAE network configuration is used for Pseudocount.
|Environment||Algorithm||Hyperparameters Used||Hyperparameters Considered|
|N/A (Default SAC hyperparameters)|
|SAC||SAC reward scale: 0.1||SAC reward scale: 0.1, 1, 10, 100|
|ICM||Learning rate: 1e-3||Learning rate: 1e-4, 1e-3, 1e-2|
|Manip.-Half||All||SAC reward scale: 0.1||(Best reward scale for Manip.-Uniform)|
|ICM||Learning rate: 1e-3||Learning rate: 1e-4, 1e-3, 1e-2|
|2D Navigation||All||SAC reward scale: 25||SAC reward scale: 1e-2, 0.1, 1, 10, 25, 100|
|3D Navigation||All||SAC reward scale: 25||SAC reward scale: 1e-2, 0.1, 1, 10, 25, 100|
|# Clusters||# GPU’s||GPU||CUDA||NVIDIA Driver|
|3||4||GeForce RTX 2080 Ti||10.1||418.43|