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Title: Safe Policy Learning for Continuous Control

Paper content:
We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i.e.,~policies that keep the agent in desirable situations, both during training and at convergence.
We formulate these problems as {\em constrained} Markov decision processes (CMDPs) and present safe policy optimization algorithms that are based on a Lyapunov approach to solve them.
Our algorithms can use any standard policy gradient (PG) method, such as deep deterministic policy gradient (DDPG) or proximal policy optimization (PPO), to train a neural network policy, while guaranteeing near-constraint satisfaction for every policy update by projecting either the policy parameter or the selected action onto the set of feasible solutions induced by the state-dependent linearized Lyapunov constraints.
Compared to the existing constrained PG algorithms, ours are more data efficient as they are able to utilize both on-policy and off-policy data.
Moreover, our action-projection algorithm often leads to less conservative policy updates and allows for natural integration into an end-to-end PG training pipeline.
We evaluate our algorithms and compare them with the state-of-the-art baselines on several simulated (MuJoCo) tasks, as well as a real-world robot obstacle-avoidance problem, demonstrating their effectiveness in terms of balancing performance and constraint satisfaction.
The field of reinforcement learning (RL) has witnessed tremendous success in many high-dimensional control problems, including video games (Mnih et al., 2015) , board games (Silver et al., 2016) , robot locomotion (Lillicrap et al., 2016) , manipulation (Levine et al., 2016; Kalashnikov et al., 2018) , navigation (Faust et al., 2018) , and obstacle avoidance (Chiang et al., 2019) .
In RL, the ultimate goal is to optimize the expected sum of rewards/costs, and the agent is free to explore any behavior as long as it leads to performance improvement.
Although this freedom might be acceptable in many problems, including those involving simulators, and could expedite learning a good policy, it might be harmful in many other problems and could cause damage to the agent (robot) or to the environment (objects or people nearby).
In such domains, it is absolutely crucial that while the agent optimizes long-term performance, it only executes safe policies both during training and at convergence.
A natural way to incorporate safety is via constraints.
A standard model for RL with constraints is constrained Markov decision process (CMDP) (Altman, 1999) , where in addition to its standard objective, the agent must satisfy constraints on expectations of auxiliary costs.
Although optimal policies for finite CMDPs with known models can be obtained by linear programming (Altman, 1999) , there are not many results for solving CMDPs when the model is unknown or the state and/or action spaces are large or infinite.
A common approach to solve CMDPs is to use the Lagrangian method (Altman, 1998; Geibel & Wysotzki, 2005) , which augments the original objective function with a penalty on constraint violation and computes the saddle-point of the constrained policy optimization via primal-dual methods (Chow et al., 2017) .
Although safety is ensured when the policy converges asymptotically, a major drawback of this approach is that it makes no guarantee with regards to the safety of the policies generated during training.
A few algorithms have been recently proposed to solve CMDPs at scale while remaining safe during training.
One such algorithm is constrained policy optimization (CPO) (Achiam et al., 2017) .
CPO extends the trust-region policy optimization (TRPO) algorithm (Schulman et al., 2015a) to handle the constraints in a principled way and has shown promising empirical results in terms scalability, performance, and constraint satisfaction, both during training and at convergence.
Another class of these algorithms is by Chow et al. (Chow et al., 2018) .
These algorithms use the notion of Lyapunov functions that have a long history in control theory to analyze the stability of dynamical systems (Khalil, 1996) .
Lyapunov functions have been used in RL to guarantee closed-loop stability (Perkins & Barto, 2002; Faust et al., 2014) .
They also have been used to guarantee that a model-based RL agent can be brought back to a "region of attraction" during exploration (Berkenkamp et al., 2017) .
Chow et al. (Chow et al., 2018) use the theoretical properties of the Lyapunov functions and propose safe approximate policy and value iteration algorithms.
They prove theories for their algorithms when the CMDP is finite with known dynamics, and empirically evaluate them in more general settings.
However, their algorithms are value-function-based, and thus are restricted to discrete-action domains.
In this paper, we build on the problem formulation and theoretical findings of the Lyapunov-based approach to solve CMDPs, and extend it to tackle continuous action problems that play an important role in control theory and robotics.
We propose Lyapunov-based safe RL algorithms that can handle problems with large or infinite action spaces, and return safe policies both during training and at convergence.
To do so, there are two major difficulties that need to be addressed:
1) the policy update becomes an optimization problem over the large or continuous action space (similar to standard MDPs with large actions), and
2) the policy update is a constrained optimization problem in which the (Lyapunov) constraints involve integration over the action space, and thus, it is often impossible to have them in closed-form.
Since the number of Lyapunov constraints is equal to the number of states, the situation is even more challenging when the problem has a large state space.
To address the first difficulty, we switch from value-function-based to policy gradient (PG) algorithms.
To address the second difficulty, we propose two approaches to solve our constrained policy optimization problem (a problem with infinite constraints, each involving an integral over the continuous action space) that can work with any standard on-policy (e.g., proximal policy optimization (PPO) (Schulman et al., 2017) ) and off-policy (e.g., deep deterministic policy gradient (DDPG) (Lillicrap et al., 2016) ) PG algorithm.
Our first approach, which we call policy parameter projection or θ-projection, is a constrained optimization method that combines PG with a projection of the policy parameters onto the set of feasible solutions induced by the Lyapunov constraints.
Our second approach, which we call action projection or a-projection, uses the concept of a safety layer introduced by (Dalal et al., 2018) to handle simple single-step constraints, extends this concept to general trajectorybased constraints, solves the constrained policy optimization problem in closed-form using Lyapunov functions, and integrates this closed-form into the policy network via safety-layer augmentation.
Since both approaches guarantee safety at every policy update, they manage to maintain safety throughout training (ignoring errors resulting from function approximation), ensuring that all intermediate policies are safe to be deployed.
To prevent constraint violations due to function approximation errors, similar to CPO, we offer a safeguard policy update rule that decreases constraint cost and ensures near-constraint satisfaction.
Our proposed algorithms have two main advantages over CPO.
First, since CPO is closely connected to TRPO, it can only be trivially combined with PG algorithms that are regularized with relative entropy, such as PPO.
This restricts CPO to on-policy PG algorithms.
On the contrary, our algorithms can work with any on-policy (e.g., PPO) and off-policy (e.g., DDPG) PG algorithm.
Having an off-policy implementation is beneficial, since off-policy algorithms are potentially more data-efficient, as they can use the data from the replay buffer.
Second, while CPO is not a back-propagatable algorithm, due to the backtracking line-search procedure and the conjugate gradient iterations for computing natural gradient in TRPO, our algorithms can be trained end-to-end, which is crucial for scalable and efficient implementation (Hafner et al., 2017) .
In fact, we show in Section 3.1 that CPO (minus the line search) can be viewed as a special case of the on-policy version (PPO version) of our θ-projection algorithm, corresponding to a specific approximation of the constraints.
We evaluate our algorithms and compare them with CPO and the Lagrangian method on several continuous control (MuJoCo) tasks and a real-world robot navigation problem, in which the robot must satisfy certain constraints, while minimizing its expected cumulative cost.
Results show that our algorithms outperform the baselines in terms of balancing the performance and constraint satisfaction (during training), and generalize better to new and more complex environments.
We used the notion of Lyapunov functions and developed a class of safe RL algorithms for continuous action problems.
Each algorithm in this class is a combination of one of our two proposed projections:
θ-projection and a-projection, with any on-policy (e.g., PPO) or off-policy (e.g., DDPG) PG algorithm.
We evaluated our algorithms on four high-dimensional simulated robot locomotion MuJoCo tasks and compared them with several baselines.
To demonstrate the effectiveness of our algorithms in solving real-world problems, we also applied them to an indoor robot navigation problem, to ensure that the robot's path is optimal and collision-free.
Our results indicate that our algorithms
1) achieve safe learning,
2) have better data-efficiency,
3) can be more naturally integrated within the standard end-to-end differentiable PG training pipeline, and
4) are scalable to tackle real-world problems.
Our work is a step forward in deploying RL to real-world problems in which safety guarantees are of paramount importance.
Future work includes
1) extending a-projection to stochastic policies and
2) extensions of the Lyapunov approach to model-based RL and use it for safe exploration.

assistant: text: A general framework for incorporating long-term safety constraints in policy-based reinforcement learning