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Title: Wasserstein Robust Reinforcement Learning

Paper content:
Reinforcement learning algorithms, though successful, tend to over-fit to training environments, thereby hampering their application to the real-world.
This paper proposes $\text{W}\text{R}^{2}\text{L}$ -- a robust reinforcement learning algorithm with significant robust performance on low and high-dimensional control tasks.
Our method formalises robust reinforcement learning as a novel min-max game with a Wasserstein constraint for a correct and convergent solver.
Apart from the formulation, we also propose an efficient and scalable solver following a novel zero-order optimisation method that we believe can be useful to numerical optimisation in general.
We empirically demonstrate significant gains compared to standard and robust state-of-the-art algorithms on high-dimensional MuJuCo environments
Reinforcement learning (RL) has become a standard tool for solving decision-making problems with feedback, and though significant progress has been made, algorithms often over-fit to training environments and fail to generalise across even slight variations of transition dynamics (Packer et al., 2018; Zhao et al., 2019) .
Robustness to changes in transition dynamics is a crucial component for adaptive and safe RL in real-world environments.
Motivated by real-world applications, recent literature has focused on the above problems, proposing a plethora of algorithms for robust decisionmaking (Morimoto & Doya, 2005; Pinto et al., 2017; Tessler et al., 2019) .
Most of these techniques borrow from game theory to analyse, typically in a discrete state and actions spaces, worst-case deviations of agents' policies and/or environments, see Sargent & Hansen (2001) ; Nilim & El Ghaoui (2005) ; Iyengar (2005); Namkoong & Duchi (2016) and references therein.
These methods have also been extended to linear function approximators (Chow et al., 2015) , and deep neural networks (Peng et al., 2017) showing (modest) improvements in performance gain across a variety of disturbances, e.g., action uncertainties, or dynamical model variations.
In this paper, we propose a generic framework for robust reinforcement learning that can cope with both discrete and continuous state and actions spaces.
Our algorithm, termed Wasserstein Robust Reinforcement Learning (WR 2 L), aims to find the best policy, where any given policy is judged by the worst-case dynamics amongst all candidate dynamics in a certain set.
This set is essentially the average Wasserstein ball around a reference dynamics P 0 .
The constraints makes the problem well-defined, as searching over arbitrary dynamics can only result in non-performing system.
The measure of performance is the standard RL objective, the expected return.
Both the policy and the dynamics are parameterised; the policy parameters θ k may be the weights of a deep neural network, and the dynamics parameters φ j the parameters of a simulator or differential equation solver.
The algorithm performs estimated descent steps in φ space and -after (almost) convergence -performs an update of policy parameters, i.e., in θ space.
Since φ j may be high-dimensional, we adapt a zero'th order sampling method based extending Salimans et al. (2017) to make estimations of gradients, and in order to define the constraint set which φ j is bounded by, we generalise the technique to estimate Hessians (Proposition 2).
We emphasise that although access to a simulator with parameterisable dynamics is required, the actual reference dynamics P 0 need not be known explicitly nor learnt by our algorithm.
Put another way, we are in the "RL setting", not the "MDP setting" where the transition probability matrix is known a priori.
The difference is made obvious, for example, in the fact that we cannot perform dynamic programming, and the determination of a particular probability transition can only be estimated from sampling, not retrieved explicitly.
Hence, our algorithm is not model-based in the traditional sense of learning a model to perform planning.
We believe our contribution is useful and novel for two main reasons.
Firstly, our framing of the robust learning problem is in terms of dynamics uncertainty sets defined by Wasserstein distance.
Whilst we are not the first to introduce the Wasserstein distance into the context of MDPs (see, e.g., Yang (2017) or Lecarpentier & Rachelson (2019) ), we believe our formulation is amongst the first suitable for application to the demanding application-space we desire, that being, high-dimensional, continuous state and action spaces.
Secondly, we believe our solution approach is both novel and effective (as evidenced by experiments below, see Section 5), and does not place a great demand on model or domain knowledge, merely access to a simulator or differentiable equation solver that allows for the parameterisation of dynamics.
Furthermore, it is not computationally demanding, in particular, because it does not attempt to build a model of the dynamics, and operations involving matrices are efficiently executable using the Jacobian-vector product facility of automatic differentiation engines.
In this paper, we proposed a robust reinforcement learning algorithm capable of outperforming others in terms of test returns on unseen dynamics.
The algorithm makes use of Wasserstein constraints for policies generalising across varying domains, and considers a zero-order method for scalable solutions.
Empirically, we demonstrated superior performance against state-of-the-art from both standard and robust reinforcement learning on low and high-dimensional MuJuCo environments.
In future work, we aim to consider robustness in terms of other components of MDPs, e.g., state representations, reward functions, and others.
Furthermore, we will implement WR 2 L on real hardware, considering sim-to-real experiments.
-Sub-Case III when indices are all distinct: We have
Diagonal Elements Conclusion: Using the above results we conclude that
• Off-Diagonal Elements (i.e., when i = j): The above analysis is now repeated for computing the expectation of the off-diagonal elements of matrix B. Similarly, this can also be split into three sub-cases depending on indices:
-Sub-Case III when indices are all distinct: We have
Off-Diagonal Elements Conclusion: Using the above results and due to the symmetric properties of H, we conclude that
Finally, analysing c, one can realise that
Substituting the above conclusions back in the original approximation in Equation 11 , and using the linearity of the expectation we can easily achieve the statement of the proposition.

assistant: text: An RL algorithm that learns to be robust to changes in dynamics