Model-free trajectory optimization for reinforcement learning

Many of the recent Trajectory Optimization algorithms alternate between local approximation of the dynamics and conservative policy update. However, linearly approximating the dynamics in order to derive the new policy can bias the update and prevent convergence to the optimal policy. In this article, we propose a new model-free algorithm that backpropagates a local quadratic time-dependent Q-Function, allowing the derivation of the policy update in closed form. Our policy update ensures exact KL-constraint satisfaction without simplifying assumptions on the system dynamics demonstrating improved performance in comparison to related Trajectory Optimization algorithms linearizing the dynamics

Local Bayesian optimization of motor skills

Bayesian optimization is renowned for its sample efficiency but its application to higher dimensional tasks is impeded by its focus on global optimization. To scale to higher dimensional problems, we leverage the sample efficiency of Bayesian optimization in a local context. The optimization of the acquisition function is restricted to the vicinity of a Gaussian search distribution which is moved towards high value areas of the objective. The proposed informationtheoretic update of the search distribution results in a Bayesian interpretation of local stochastic search: the search distribution encodes prior knowledge on the optimum’s location and is weighted at each iteration by the likelihood of this location’s optimality. We demonstrate the effectiveness of our algorithm on several benchmark objective functions as well as a continuous robotic task in which an informative prior is obtained by imitation learning

Empowered skills

Robot Reinforcement Learning (RL) algorithms return a policy that maximizes a global cumulative reward signal but typically do not create diverse behaviors. Hence, the policy will typically only capture a single solution of a task. However, many motor tasks have a large variety of solutions and the knowledge about these solutions can have several advantages. For example, in an adversarial setting such as robot table tennis, the lack of diversity renders the behavior predictable and hence easy to counter for the opponent. In an interactive setting such as learning from human feedback, an emphasis on diversity gives the human more opportunity for guiding the robot and to avoid the latter to be stuck in local optima of the task. In order to increase diversity of the learned behaviors, we leverage prior work on intrinsic motivation and empowerment. We derive a new intrinsic motivation signal by enriching the description of a task with an outcome space, representing interesting aspects of a sensorimotor stream. For example, in table tennis, the outcome space could be given by the return position and return ball speed. The intrinsic motivation is now given by the diversity of future outcomes, a concept also known as empowerment. We derive a new policy search algorithm that maximizes a trade-off between the extrinsic reward and this intrinsic motivation criterion. Experiments on a planar reaching task and simulated robot table tennis demonstrate that our algorithm can learn a diverse set of behaviors within the area of interest of the tasks

Layered direct policy search for learning hierarchical skills

Solutions to real world robotic tasks often require complex behaviors in high dimensional continuous state and action spaces. Reinforcement Learning (RL) is aimed at learning such behaviors but often fails for lack of scalability. To address this issue, Hierarchical RL (HRL) algorithms leverage hierarchical policies to exploit the structure of a task. However, many HRL algorithms rely on task specific knowledge such as a set of predefined sub-policies or sub-goals. In this paper we propose a new HRL algorithm based on information theoretic principles to autonomously uncover a diverse set of sub-policies and their activation policies. Moreover, the learning process mirrors the policys structure and is thus also hierarchical, consisting of a set of independent optimization problems. The hierarchical structure of the learning process allows us to control the learning rate of the sub-policies and the gating individually and add specific information theoretic constraints to each layer to ensure the diversification of the subpolicies. We evaluate our algorithm on two high dimensional continuous tasks and experimentally demonstrate its ability to autonomously discover a rich set of sub-policies

Model-Free Trajectory-based Policy Optimization with Monotonic Improvement

Many of the recent trajectory optimization algorithms alternate between linear approximation of the system dynamics around the mean trajectory and conservative policy update. One way of constraining the policy change is by bounding the Kullback-Leibler (KL) divergence between successive policies. These approaches already demonstrated great experimental success in challenging problems such as end-to-end control of physical systems. However, the linear approximation of the system dynamics can introduce a bias in the policy update and prevent convergence to the optimal policy. In this article, we propose a new model-free trajectory-based policy optimization algorithm with guaranteed monotonic improvement. The algorithm backpropagates a local, quadratic and time-dependent Q-Function learned from trajectory data instead of a model of the system dynamics. Our policy update ensures exact KL-constraint satisfaction without simplifying assumptions on the system dynamics. We experimentally demonstrate on highly non-linear control tasks the improvement in performance of our algorithm in comparison to approaches linearizing the system dynamics. In order to show the monotonic improvement of our algorithm, we additionally conduct a theoretical analysis of our policy update scheme to derive a lower bound of the change in policy return between successive iterations

Model-Free Trajectory-based Policy Optimization with Monotonic Improvement

Many of the recent trajectory optimization algorithms alternate between linear approximation of the system dynamics around the mean trajectory and conservative policy update. One way of constraining the policy change is by bounding the Kullback-Leibler (KL) divergence between successive policies. These approaches already demonstrated great experimental success in challenging problems such as end-to-end control of physical systems. However, these approaches lack any improvement guarantee as the linear approximation of the system dynamics can introduce a bias in the policy update and prevent convergence to the optimal policy. In this article, we propose a new model-free trajectory-based policy optimization algorithm with guaranteed monotonic improvement. The algorithm backpropagates a local, quadratic and time-dependent Q-Function learned from trajectory data instead of a model of the system dynamics. Our policy update ensures exact KL-constraint satisfaction without simplifying assumptions on the system dynamics. We experimentally demonstrate on highly non-linear control tasks the improvement in performance of our algorithm in comparison to approaches linearizing the system dynamics. In order to show the monotonic improvement of our algorithm, we additionally conduct a theoretical analysis of our policy update scheme to derive a lower bound of the change in policy return between successive iterations

Projections for Approximate Policy Iteration Algorithms

Approximate policy iteration is a class of reinforcement learning (RL) algorithms where the policy is encoded using a function approximator and which has been especially prominent in RL with continuous action spaces. In this class of RL algorithms, ensuring increase of the policy return during policy update often requires to constrain the change in action distribution. Several approximations exist in the literature to solve this constrained policy update problem. In this paper, we propose to improve over such solutions by introducing a set of projections that transform the constrained problem into an unconstrained one which is then solved by standard gradient descent. Using these projections, we empirically demonstrate that our approach can improve the policy update solution and the control over exploration of existing approximate policy iteration algorithms

Learning Replanning Policies with Direct Policy Search

Direct policy search has been successful in learning challenging real world robotic motor skills by learning open-loop movement primitives with high sample efficiency. These primitives can be generalized to different contexts with varying initial configurations and goals. Current state-of-the-art contextual policy search algorithms can however not adapt to changing, noisy context measurements. Yet, these are common characteristics of real world robotic tasks. Planning a trajectory ahead based on an inaccurate context that may change during the motion often results in poor accuracy, especially with highly dynamical tasks. To adapt to updated contexts, it is sensible to learn trajectory replanning strategies. We propose a framework to learn trajectory replanning policies via contextual policy search and demonstrate that they are safe for the robot, that they can be learned efficiently and that they outperform non-replanning policies for problems with partially observable or perturbed contex

Sample and feedback efficient hierarchical reinforcement learning from human preferences

While reinforcement learning has led to promising results in robotics, defining an informative reward function can sometimes prove to be challenging. Prior work considered including the human in the loop to jointly learn the reward function and the optimal policy. Generating samples from a physical robot and requesting human feedback are both taxing efforts for which efficiency is critical. In contrast to prior work, in this paper we propose to learn reward functions from both the robot and the human perspectives in order to improve on both efficiency metrics. On one side, learning a reward function from the human perspective increases feedback efficiency by assuming that humans rank trajectories according to an outcome space of reduced dimensionaltiy. On the other side, learning a reward function from the robot perspective circumvents the need for learning a dynamics model while retaining the sample efficiency of model-based approaches. We provide an algorithm that incorporates bi-perspective reward learning into a general hierarchical reinforcement learning framework and demonstrate the merits of our approach on a toy task and a simulated robot grasping task

Compatible natural gradient policy search

Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural gradient policy update. We show that the natural gradient and trust region optimization are equivalent if we use the natural parameterization of a standard exponential policy distribution in combination with compatible value function approximation. Moreover, we show that standard natural gradient updates may reduce the entropy of the policy according to a wrong schedule leading to premature convergence. To control entropy reduction we introduce a new policy search method called compatible policy search (COPOS) which bounds entropy loss. The experimental results show that COPOS yields state-of-the-art results in challenging continuous control tasks and in discrete partially observable tasks