Visual Adversarial Imitation Learning using Variational Models

1 Introduction . The ability of reinforcement learning ( RL ) agents to autonomously learn by interacting with the environment presents a promising approach for learning diverse skills . However , reward specification has remained a major challenge in the deployment of RL in practical settings [ 1 , 2 , 3 ] . The ability to imitate humans or other expert trajectories allows us to avoid the reward specification problem , while also circumventing challenges related to task-specific exploration in RL . Visual demonstrations can also be a more natural way to teach robots various tasks and skills in real-world applications . However , this setting is also fraught with a number of technical challenges including representation learning for visual observations , sample complexity due to the high dimensional observation spaces , and learning instability [ 4 , 5 , 6 ] due to lack of a stationary learning signal . We aim to overcome these challenges and to develop an algorithm that can learn from limited demonstration data and scale to high-dimensional observation and action spaces often encountered in robotics applications . Behaviour cloning ( BC ) is a classic algorithm to imitate expert demonstrations [ 7 ] , which uses supervised learning to greedily match the expert behaviour at demonstrated expert states . Due to environment stochasticity , covariate shift , and policy approximation error , the agent may drift away from the expert state distribution and ultimately fail to mimic the demonstrator [ 8 ] . While a wide initial state distribution [ 9 ] or the ability to interactively query the expert policy [ 8 ] can circumvent 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . these difficulties , such conditions require additional supervision and are difficult to meet in practical applications . An alternate line of work based on inverse RL [ 10 , 11 ] and adversarial imitation learning [ 12 , 13 ] aims to not only match actions at demonstrated states , but also the long term state visitation distribution of the expert [ 14 ] . Adversarial imitation learning approaches explicitly train a GAN-based classifier [ 15 ] to distinguish the visitation distribution of the agent from the expert , and use it as a reward signal for training the agent with RL . While these methods have achieved substantial improvement over behaviour cloning without additional expert supervision , they are difficult to deploy in realistic scenarios for multiple reasons : ( 1 ) the objective requires on-policy data collection leading to high sample complexity ; ( 2 ) the reward function changes as the RL agent learns ; and ( 3 ) high-dimensional observation spaces require representation learning and exacerbate the optimization challenges . Our main contribution in this work is the development of a new algorithm , variational model-based adversarial imitation learning ( V-MAIL ) , which aims to overcome each of the aforementioned challenges within a single framework . As illustrated in Figure 1 , V-MAIL trains a variational latent-space dynamics model and a discriminator that provides a learning reward signal by distinguishing latent rollouts of the agent from the expert . The key insight of our approach is that variational models can address these challenges simultaneously by ( a ) making it possible to collect on-policy roll-outs inside the model without environment interaction , leading to an efficient and stable optimization process and ( b ) providing a rich auxiliary objective for efficiently learning compact state representations and which regularizes the discriminator . Furthermore , the variational model also allows V-MAIL to perform zero-shot transfer to new imitation learning tasks . By generating on-policy rollouts within the model , and training the discriminator using these rollouts along with demonstrations of a new task , V-MAIL can learn policies for new tasks without any additional environment interactions . Through experiments on vision-based locomotion and manipulation tasks , we find that V-MAIL can successfully learn visuomotor control policies from limited demonstrations . In particular , V-MAIL exhibits stable and near-monotonic learning , is highly sample efficient , and asymptotically matches the expert level performance on most tasks . In contrast , prior algorithms exhibit unstable learning and poor asymptotic performance , often achieving less that 20 % of expert performance on these visionbased tasks . We further show the ability to transfer the model to novel tasks , acquiring qualitatively new behaviors using only a few demonstrations and no additional environment interactions . 2 Preliminaries . We consider the problem setting of learning in partially observed Markov decision processes ( POMDPs ) , which can be described with the tuple : M = ( S , A , X , R , T , U , γ ) , where s ∈ S is the state space , a ∈ A is the action space , x ∈ X is the observation space and r = R ( s , a ) is a reward function . The state evolution is Markovian and governed by the dynamics as s′ ∼ T ( ·|s , a ) . Finally , the observations are generated through the observation model x ∼ U ( ·|s ) . The widely studied Markov decision process ( MDP ) is a special case of this 7-tuple where the underlying state is directly observed in the observation model . In this work , we study imitation learning in unknown POMDPs . Thus , we do not have access to the underlying dynamics , the true state representation of the POMDP , or the reward function . In place of the rewards , the agent is provided with a fixed set of expert demonstrations collected by executing an expert policy πE , which we assume is optimal under the unknown reward function . The agent can interact with the environment and must learn a policy π ( at|x≤t ) that mimics the expert . 2.1 Imitation learning as divergence minimization . In line with prior work , we interpret imitation learning as a divergence minimization problem [ 12 , 14 , 16 ] . For simplicity of exposition , we consider the MDP case in this section , and discuss POMDP extensions in Section 3.2 . Let ρπM ( s , a ) = ( 1 − γ ) ∑∞ t=0 γ tP ( st = s , at = a ) be the discounted state-action visitation distribution of a policy π in MDP M. Then , a divergence minimization objective for imitation learning corresponds to min π D ( ρπM , ρEM ) , ( 1 ) where ρEM is the discounted visitation distribution of the expert policy π E , and D is a divergence measure between probability distributions such as KL-divergence , Jensen-Shannon divergence , or a generic f−divergence . To see why this is a reasonable objective , let J ( π , M ) denote the expected value of a policy π in M. Inverse RL [ 17 , 12 , 13 ] interprets the expert as the optimal policy under some unknown reward function . With respect to this unknown reward function , the sub-optimality of any policy π can be bounded as : ∣∣∣J ( πE , M ) − J ( π , M ) ∣∣∣ ≤ Rmax 1− γ DTV ( ρπM , ρEM ) , since the policy performance is ( 1− γ ) · J ( π , M ) = E ( s , a ) ∼ρπM [ r ( s , a ) ] . We use DTV to denote total variation distance . Since various divergence measures are related to the total variation distance , optimizing the divergence between visitation distributions in state space amounts to optimizing a bound on the policy sub-optimality . 2.2 Generative Adversarial Imitation Learning ( GAIL ) . With the divergence minimization viewpoint , any standard generative modeling technique including density estimation , VAEs , GANs etc . can in principle be used to minimize Eq . 1 . However , in practice , use of certain generative modeling techniques can be difficult . A standard density estimation technique would involve directly parameterizing ρπM , say through auto-regressive flows , and learning the density model . However , a policy that induces the learned visitation distribution in M is not guaranteed to exist and may prove hard to recover . Similar challenges prevent the direct application of a VAE based generative model as well . In contrast , GANs allow for a policy based parameterization , since it only requires the ability to sample from the generative model and does not require the likelihood . This approach was followed in GAIL , leading to the optimization max π min Dψ E ( s , a ) ∼ρEM [ − logDψ ( s , a ) ] + E ( s , a ) ∼ρπM [ − log ( 1−Dψ ( s , a ) ) ] , ( 2 ) where Dψ is a discriminative classifier used to distinguish between samples from the expert distribution and the policy generated distribution . Results from Goodfellow et al . [ 15 ] and Ho and Ermon [ 12 ] suggest that the learning objective in Eq . 2 corresponds to the divergence minimization objective in Eq . 1 with Jensen-Shannon divergence . In order to estimate the second expectation in Eq . 2 we require on-policy samples from π , which is often data-inefficient and difficult to scale to high-dimensional image observations . Some off-policy algorithms [ 18 , 19 ] replace the expectation under the policy distribution with expectation under the current replay buffer distribution , which allows for off-policy training , but can no longer guarantee that the induced visitation distribution of the learned policy will match that of the expert . 3 Variational Model-Based Adversarial Imitation Learning . Imitation learning methods based on expert distribution matching have unique challenges . Improving the generative distribution of trajectories ( through policy optimization , as we do not have control over the environment dynamics ) requires samples from ρπM , which requires rolling out π in the environment . Furthermore , the optimization landscape of a saddle point problem ( see Eq . 2 ) can require many iterations of learning , each requiring fresh on-policy rollouts . This is different from typical generative modeling applications [ 15 , 20 ] where sampling from the generator is cheap . To overcome these challenges , we present a model-based imitation learning algorithm . Model-based algorithms can utilize a large number of synthetic on-policy rollouts using the learned dynamics model , with periodic model correction . In addition , learning the dynamics model serves as a rich auxiliary task for state representation learning , making policy learning easier and more sample efficient . For conceptual clarity and ease of exposition , we first present our conceptual algorithm in the MDP setting in Section 3.1 , and then extend this algorithm to the POMDP case in Section 3.2 . Finally , we present a practical version of our algorithm in Sections 3.3 and 3.4 . 3.1 Model-Based Adversarial Imitation Learning . Model-based algorithms for RL and IL involve learning an approximate dynamics model T̂ using environment interactions . The learned dynamics model can be used to construct an approximate MDP M̂ . In our context of imitation learning , learning a dynamics model allows us to generate samples from M̂ as a surrogate for samples from M , leading to the objective : min π D ( ρπM̂ , ρ E M ) , ( 3 ) which can serve as a good proxy to Eq . 1 as long as the model approximation is accurate . This intuition can be captured using the following lemma ( see the appendix for proof ) . Lemma 1 . ( Simultaneous policy and model deviation ) Suppose we have an α-approximate dynamics model given by DTV ( T̂ ( s , a ) , T ( s , a ) ) ≤ α ∀ ( s , a ) . Let Rmax = max ( s , a ) R ( s , a ) be the maximum of the unknown reward in the MDP with unknown dynamics T . For any policy π , we can bound the sub-optimality with respect to the expert policy πE as : ∣∣∣J ( πE , M ) − J ( π , M ) ∣∣∣ ≤ Rmax 1− γ DTV ( ρπM̂ , ρ E M ) + α ·Rmax ( 1− γ ) 2 . ( 4 ) Thus , the divergence minimization in Eq . 3 serves as an approximate bound on the sub-optimality with a bias that is proportional to the model error . Thus , we ultimately propose to solve the following saddle point optimization problem : max π min Dψ E ( s , a ) ∼ρEM [ − logDψ ( s , a ) ] + E ( s , a ) ∼ρπ M̂ [ − log ( 1−Dψ ( s , a ) ) ] , ( 5 ) which requires generating on-policy samples only from the learned model M̂ . We can interleave policy learning according to Eq . 5 with performing policy rollouts in the real environment to iteratively improve the model . Provided the policy is updated sufficiently slowly , Rajeswaran et al . [ 21 ] show that such interleaved policy and model learning corresponds to a stable and convergent algorithm , while being highly sample efficient .

Summary: The authors present a novel method for imitation learning from a limited set of expert demonstrations. The method is heavily based on the DREAMER model-based RL framework and extends this with a discriminator to match a learner policy to an expert rollout. I find the paper overall well-written and a worthwhile contribution but there are some technicalities holding the paper back from being a top submission. I am overall still in favor of getting this work accepted since I believe most things can be addressed easily and I'm happy to change my rating if the authors can address some of my concerns.