R ECONNAISSANCE FOR R EINFORCEMENT L EARNING WITH S AFETY C ONSTRAINTS
Anonymous authors
Paper under double-blind review
A BSTRACT
Practical reinforcement learning problems are often formulated as constrained
Markov decision process (CMDP) problems, in which the agent has to maximize the expected return while satisfying a set of prescribed safety constraints.
In this study, we consider a situation in which the agent has access to the generative model which provides us with a next state sample for any given state-action pair, and propose a model to solve a CMDP problem by decomposing the CMDP into a pair of MDPs; reconnaissance MDP (R-MDP) and planning MDP (P-MDP).
In R-MDP, we train threat function , the Q-function analogue of danger that can determine whether a given state-action pair is safe or not.
In P-MDP, we train a reward-seeking policy while using a ﬁxed threat function to determine the safeness of each action.
With the help of generative model, we can efﬁciently train the threat function by preferentially sampling rare dangerous events.
Once the threat function for a baseline policy is computed, we can solve other CMDP problems with different reward and different danger-constraint without the need to re-train the model.
We also present an efﬁcient approximation method for the threat function that can greatly reduce the difﬁculty of solving R-MDP.
We will demonstrate the efﬁcacy of our method over classical approaches in benchmark dataset and complex collision-free navigation tasks.
I NTRODUCTION
With recent advances in reinforcement learning (RL), it is becoming possible to learn complex reward-maximizing policy in an increasingly more complex environment (Mnih et al., 2015; Silver et al., 2016; Andrychowicz et al., 2018; James et al., 2018;
Kalashnikov et al., 2018).
However, it is difﬁcult in general to assess whether the policies found by a given RL algorithm is physically safe when applied to real world situations.
This has long been one of the greatest challenges in the application of reinforcement learning to mission-critical systems.
In a popular setup, one assumes a
Markovian system together with a predeﬁned measure of danger, and formulates the problem as a type of constrained Markov decision process (CMDP) problem.
That is, based on the classical RL notations in which π represents a policy of the agent, we aim to solve
[Equation]
where h is a trajectory of state-action pairs, R ( h ) is the total return that can be obtained by h , and
D ( h ) is the measure of how dangerous the trajectory h is.
To solve this problem, one must monitor the value of E π [ D ( h )] throughout the training.
Methods like (Altman, 1999; Geibel & Wysotzki, 2005; Geibel, 2006; Achiam et al., 2017a; Chow et al., 2018; 2019) uses sampling to approximate
E π
[ D ( h )] or its Lyapunov function at every update.
However, the sample-based evaluation of the
E π
[ D ( h )] is particularly difﬁcult when the system involves “rare" catastrophic accidents, because an immense number of samples will be required to collect information about the cause of such accident.
This problem can be partially resolved if we can use a generative model to predict the outcome of any given sequence of actions and initial state.
Model Predictive Control (MPC) (Maciejowski, 2002;
Falcone et al., 2007; Wang & Boyd, 2010; Di Cairano et al., 2013;
Weiskircher et al., 2017) uses the philosophy of receding horizon and predicts the future outcome of actions in order to determine what action the agent should take in the next step.
If the future-horizon to consider is sufﬁciently short and the dynamics is deterministic, the prediction can often be approximated well by linear dynamics,
[Figure]
which can be evaluated instantly.
However, because MPC must ﬁnish its assessment of the future before taking every action, its performance is limited by the speed of the predictions.
When we apply MPC to environments with multiple agents and stochastic dynamics, the computational load of prediction is especially heavy and it can be difﬁcult to ﬁnish the prediction in time.
MPC requires this computation for each time-step even if the current state is similar to the ones experienced in the past.
Meanwhile, if the prediction is done for only a short horizon, MPC may suggest a move to a state leading to a catastrophe.
In an effort to reduce the difﬁculty in evaluating the safeness of policies, we propose a novel generative model-based approach that looks for a solution of a CMDP problem by decomposing the CMDP into a pair of MDPs: a reconnaissance MDP (R-MDP) and planning MDP (P-MDP).
The purpose of
R-MDP is to (1) recon the state space using the generative model and (2) train a baseline policy for the threat function , which is a Q-function analogue of D .
In R-MDP, we use generative model to selectively sample trajectories containing rare dangerous events, and learn the threat function for the baseline policy in the way of supervised learning.
Once we obtain a good approximation of the threat function for the baseline policy, we can determine whether a given action is safe at each state or not by just evaluating the threat function.
This process does not involve prediction, which can be computationally demanding.
We will theoretically show that we can increase the set of safe actions by improving the safeness of the baseline policy .
In
P-MDP, we train the reward-seeking policy while using the threat function to make sure that unsafe actions are not chosen.
We may say that P-MDP is a version of original MDP in which the agents are only allowed to select an action from the set of safe policies deﬁned by the threat function.
P-MDP can be solved with standard RL methods like DQN (Mnih et al., 2015).
With our framework, the user is freed from the need of monitoring E π
[ D ] throughout the whole training process.
We will also show that our approach enjoys the following useful properties: 1) If we can ﬁnd a safe baseline policy from the R-MDP problem, the learning of P-MDP will always be safe. 2)
So long that we deﬁne the danger with the same D function, we can re-use the threat function constructed for one CMDP problem to solve another CMDP problem with a different reward function and different constraint threshold.
When dealing with a problem with multiple sources of danger, we can use a basic rule of probability to upper-bound the threat functions by a sum of sub-threat functions, with each summand corresponding to different source of danger each.
The property (2) allows us to train an agent that can safely navigate a circuit irrespective of the course-layout1(d).
In this experiment, we represented the circuit’s wall as a set of point obstacles, and computed the threat functions for the collision with each obstacle point.
The property (3) allows us to ﬁnd a good reward-seeking policy for a sophisticated task like safely navigating through a crowd of randomly moving obstacles .
Although our method does not guarantee to ﬁnd the optimal solution of the CMDP problem, there has not been any study to date that has succeeded in solving a CMDP in dynamical environments as high-dimensional as the ones discussed in this study.
P ROBLEM FORMULATION AND THEORETICAL RESULTS
We assume that the system in consideration is a discrete-time constrained Markov Decision Process (CMDP) with ﬁnite horizon, deﬁned by a tuple ( S, A, r, d, P, P 0 ) , where S is the set of states, A is the set of actions, P ( s (cid:48) | s, a ) is the density of the state transition probability from s to s (cid:48) when the action is a , r ( s, a ) is the reward obtained by action a at state s , d ( s, a ) is the non-negative danger of taking action a at state s , and P 0 is the distribution of the initial state.
We use π ( a | s ) to denote the policy
π ’s probability of taking an action a at a state s .
Finally, let us use 1 B to represent the indicator function of an event
B.
Now we formally present the optimization problem (1).
[Equation]
where c ≥ 0 speciﬁes the safety level, γ, β ∈ [0 , 1) are the discount factors, and E π [ · ] denotes the expectation with respect to π , P and P 0 .
We use plain E to denote the expectation with respect to P 0 .
2. P ROPERTIES OF THREAT FUNCTIONS AND SECURE POLICIES
In our formulation, we use the following threat function and danger function as a danger-analogue of the action-value function and state-value function, respectively.
[Equation]
We say that a policy η is safe if E [ D η 0 ( s 0 )] ≤ c .
Indeed, the set of safe policies is the set of feasible policies for the CMDP (1).
Before we proceed further, we describe several key deﬁnitions and theorems that stem from the deﬁnition of the threat function.
For now, let us consider a timedependent safety threshold x t deﬁned at each time t , and let η be any policy.
Let us also use x to denote ( x 0 , . . .
, x T − 1 ) .
Then the set of ( η, x ) -secure actions is the set of actions that are deemed safe by η for the safety threshold x in the sense of the following deﬁnition;
Deﬁnition 1 ( ( η, x ) -secure actions and ( η, x ) -secure states) .
Let A η, x ( s, t ) =
(cid:110)
a ; T ηt ( s, a
[Equation]
≤ x t (cid:111) .
[Equation]
This ( η, x ) -secure set of actions
A η, x ( s ) represents the agent’s freedom in seeking the reward under the safety protocol created by the policy η .
The set of secure actions for an arbitrary η could be empty for some states.
( η, x ) -secure states is deﬁned as a set of states for which there is non-empty ( η, x ) - secure actions.
If we use supp( p ) to denote the support of a distribution p , we can use this deﬁnition to deﬁne a set of policies that is at least as safe as η .
First, let us deﬁne the set of distributions,
[Equation]
Then the following set of policies are at least as safe as η.
Deﬁnition 2 (General ( η, x ) -secure policies) .
[Equation]
Now, we are ready to develop our theory for determining when a given policy is safe.
The following theorem enables us to bound D πt ( s t ) without evaluating the expectation with respect to π .
Theorem 1.
For a given policy η and a sequence of safety thresholds x
= ( x 0 , . . .
, x T − 1 ) , let π be a policy in Π η, x G .
Let us use d TV ( p, q ) to denote the total variation distance 1 between two distributions p and q .
Then for all t ∈ { 0 , . .
, T − 1 }
[Equation]
where z t = 1 s t ∈ S η, x d TV ( π ( ·| s t ) , η ( ·| s t )) is a distance measure of the two polices.
The proof of this result uses practically same logic as the one used for Theorem 1 in (Achiam et al., 2017a).
Please see the Appendix for the detail.
In practical application, it is more convenient to set x t = x for all t .
If we also bound z t from above by 1 , we obtain the following useful result.
Corollary 2.
If E [ D η 0 ( s 0 )] ≤ c , let x = ( x ηc , . . .
, x ηc ) with x ηc = 12 ( c − E
[ D η 0 ( s 0 )]) 1 − β 1 − β T .
Then a policy π is safe if π ∈ Π η, x ηc G , (i.e
[Equation]
The above corollary provides us a safety guarantee for π when η itself is safe.
But in fact, if we look inside a smaller subset of Π η, x G , we can guarantee the safety even when η itself may not be safe.
Deﬁnition 3 ( ( η, x ) -secure policies) .
Let greedy- η ( a | s )
=
a =argmin a (cid:48) T ηt ( s,a (cid:48) ) .
Π η, x = (cid:8) π ; for s ∈ S η, x , supp( π ( ·| s )) ⊆
A η, x ( s ) , otherwise, π ( ·| s ) = greedy- η ( ·
[Equation]
s
[Equation]
The ( η, x ) -secure policies Π η, x is indeed a subset of Π η, x G because greedy- η is just the one-step policy improvement from η .
It turns out that we can construct a pool of absolutely safe policies explicitly from T ηt ( s, a ) and c alone even when η itself is not necessarily safe.
Corollary 3.
If E [ D greedy- η( s 0 )] ≤ c , by setting x = ( x ηc,g , . . .
, x ηc,g ) with x ηc,g = 12 ( c −
E [ D greedy- η( s 0 )]) 1 − β 1 − β T , any policy π ∈ Π η, x ηc,g is safe, i.e., E [ D π 0 ( s 0 )] ≤ c .
This result can be derived using a result similar to Eq.(6)
(See Appendix for the detail).
In the next section, we will use Π η, x ηc,g to construct a pool of safe policies from which to seek a good and safe reward-seeking policy.
Now, several remarks are in order.
First, if we set β = 1 , then x ηc → 0 as
T → ∞ .
This is in agreement with the law of large numbers; that is, any accident with positive probability is bound to happen at some point.
Also, note that we have A η, x ( s ) ⊆
A η (cid:48) , x ( s ) whenever
T η (cid:48) t ( s, a ) ≤ T ηt ( s, a ) for any t .
Thus, by ﬁnding the risk-minimizing η , we can maximize the pool of safe policies.
Whenever we can, we shall therefore look not just for a safe η , but also for the threat minimizing policy.
Finally and most importantly, note that the threshold expression in Corollary 3 is free of π .
We can use this result to tackle the CMDP problem by solving two separate MDP problems.
More particularly, in seeking a solution to the CMDP problem we can (i) ﬁrst look for an η satisfying E
[ D greedy- η( s 0 )] ≤ c , and then (ii) look for a safe reward maximizing policy π in Π η .
We will further articulate this procedure in the next section.
Hereafter unless otherwise noted, we will use Π η to denote Π η, x ηc,g , and use S η , A η ( s ) to denote S η, x ηc,g , A η, x ηc,g ( s ) .
R ECONNAISSANCE -MDP (R-MDP) AND P LANNING -MDP (P-MDP)
In the previous section, we have shown that we can create a pool of safe policies using a baseline safe policy.
Also, by training a policy to minimize the threat, we can ﬁnd a safe policy that corresponds to a larger pool of secure policies, and we look within this pool to search for a possibly better reward seeking policy.
This motivates us to solve two types of MDP problem: the one with the aim of minimizing the threat, and the one with the goal of maximizing the reward.
The purpose of the Reconnaissance MDP (R-MDP) is thus to reconnoiter the system prior to the reward maximization process and to look for the threat minimization safe policy η ∗ that solves
[Equation]
Indeed, solution η ∗ of argmin is not unique, up to freedom of the actions on the states unreachable by any optimal policy η ∗ .
For our ensuing discussions, we will chose η ∗ to be a version whose policy on each unreachable state s ∗ is computed by (8) with initial state being s ∗ .
If the problem is of inﬁnite horizon, η ∗ computed from any initial state will be same because of the Markov property.
As a process, R-MDP is same as the original MDP except that we have a danger function in place of a reward function, and that the goal of the agent in the system is to minimize the danger.
The following is true in general about R-MDP.
Corollary 4.
If the set of feasible policies of the original CMDP is nonempty, the optimal R-MDP policy η ∗ is safe.
Thus, every policy in Π η ∗ is safe.
After we solve the R-MDP, the Planning MDP (P-MDP) searches within Π η ∗ for a good rewardseeking policy π ∗ .
The P-MDP is similar to the original MDP except that the agent is only allowed to take actions from A η ∗ when s ∈ S η ∗ and that it follows greedy- η ∗ at non-secure states s (cid:54)∈ S η ∗ .
[Equation]
In implementation, we do not explicitly construct Π η ∗ .
Instead, we evaluate T η ∗ t ( s, a ) for every considered state-action pair in P-MDP and make sure that all suggested reward-seeking actions are in
A η ∗ .
Note that, if policy η ∗ is safe, every policy in Π η ∗ is guaranteed to be safe.
More particularly, in such case, any policy in the entire learning process of the P-MDP is safe.
We would refer to the whole procedure of solving R-MDP and P-MDP as Reconnaissance and Planning (RP) algorithm .
The following table summarizes the algorithm.
Naturally, whether this algorthm works in application
Algorithm 1 RP algorithm
1: Obtain the baseline policy η ∗ by either solving R-MDP or selecting a heuristic policy 2: Estimate T η ∗ t ( · , · ) 3: Solve the P-MDP (9) while referring the evaluation of T η ∗ t at every considered state-action pair so that all actions will be chosen from A η ∗ , x (Eq.
depends on how well we can evaluate the threat in R-MDP.
However, in many applications, the set of dangerous events to be avoided can be extremely rare.
These rare events make the empirical approximation E π [ · ] difﬁcult.
We resolve this problem by evaluating T ηt ( s, a ) with the generative model.
With the generative model, we can freely explore the system from any arbitrary initial state, and evaluate T ηt ( s, a ) for any ( s, a ) and η .
To facilitate the learning of T η ∗ t ( s, a ) , we use the generative model to preferentially sample the states with relatively high estimated T η ∗ t ( s, a ) more frequently.
We will next introduce a technique to approximate the threat function.
3. T HREAT DECOMPOSITION AND APPROXIMATE RP ALGORITHM
We will present a useful bound on the threat function that can be used when the danger is be described in terms of multiple risky events.
Suppose that there are N risky events { E 1 , ..., E N } to consider, and that the goal is optimize the reward while avoiding any risky event.
More formally, let us represent the indicator function of E n by d n ( s ( o ) t , s ( n ) t , a t ) where s ( n ) t is the state of the system relevant to E n and s ( o ) is the state of the system not relevant to any risky events.
Let us also assume that the transition probabilities decompose into p ( s t +1 | s t , a t )
= p ( s ( o ) t +1 | s ( o ) t , a t ) (cid:81)
Nn =1 p ( s ( n ) t +1 | s ( n ) t , s ( o ) t , a t ) .
If d is the indicator function of ∪ mn =1 E n , we can formalize this result as follows:
Theorem 5.
Let η be a policy that takes action based solely on s ( o ) t , and let T η be the threat function deﬁned for the indicator function of ∪ Nn =1 E n .
Then T ηt ( s t , a t ) ≤ (cid:80) Nn =1 T η,nt ( s ( o ) t , s ( n ) t , a t ) .
This result is especially useful in navigation-like tasks.
For example, when E n is the collision event with n th obstable, s n will be the state of n th obstacle, and s o will be the aggregate state of the system that are not related to any obstacles(state of the agent, etc).
In such case, each T η,n can be estimated using the simulator containing just the n th obstable object and the agent.
Algorithm 2 is the algorithm that uses Theorem 5.
For other variants of Theorem 5, please see the Appendix.
Theorem 5 suggests
Algorithm 2 Approximate RP algorithm 1: Pick
a heuristic policy η ( o ) which depends on s ( o ) only.
2: Estimate the threat functions T η ( o ) ,n t ( · , · ) for all sub R-MDPs.
3: Solve the P-MDP (9) while referring to the evaluation of (cid:80) n T η ( o ) ,n t at every considered state-action pair so that all chosen actions will satisfy (cid:80) n T η ( o ) ,n t
≤ x t .
that that
threat
function can be treated like risk
potential.
Wolf & Burdick
Rasekhipour
et al
(2016b)
; Ji et al.
Risk-potential based methods also work by evaluating the risk of collision with each obstacle for each location in the environment, and by superimposing the results.
However, most of these works deﬁne the risk potential rather heuristically.
[Figure]
E XPERIMENT
We conducted a series of experiments to (i) analyze the nature of the threat function, (ii) the effect of the choice of the baseline policy η on the performance of P-MDP
[Figure]
and (iii) the sheer efﬁcacy of the
RP algorithm implemented with the threat decomposition approximation.
We will also show that we can re-use the threat function computed for one CMDP to solve another CMDP problem with similar safety constraint.
We will demonstrate the power of our method on high-dimensional problems.
4. F ROZEN L AKE E NVIRONMENT
We considered an example of the frozen lake environment FrozenLake8x8-v0 in OpenAI Gym where the agent navigates in the 8 by 8 grid world in Fig.
2 (a).
The goal of the agent is to start from ‘S’ and navigate its way through the slippy environment to reach the goal ‘G’ while avoiding the holes ‘H’.
Each time on a frozen surface ‘F’, the agent can choose to move in one of the four directions, but the surface is slippery so the agent’s actual movement may be different from the intended direction.
We ﬁrst construct the R-MDP as described in Sec 3.
The R-MDP is a tabular MDP which can be solved by value iteration.
The threat function of the optimal R-MDP policy is shown in Fig.
2 (b).
The threat values are indeed higher when the agent is closer to the holes.
From the threat function we can see that, there is a safe path from ’S’ to ’G’ following the right most column.
But the tricky part is at positions (6, 8) and (7, 8).
On these positions, the only action that can avoid the danger is ’RIGHT’, which is not the best reward-seeking action (’DOWN’).
The P-MDP is again a tabular MDP, and we can solve it by value iteration.
In Fig.
2 (c) and (d), we compare the performance of P-MDP based on the optimal R-MDP policy η ∗ against those of the
P-MDPs based on the sub-optimal R-MDP policies.
Based on a completely random policy for η 0 , we deﬁned η 1 = greedy-
η 0 and η 2 = greedy-
η 1 .
In other words, both η 1 , η 2 are the outcomes of applying policy iterations to the random policy.
We also plot the reward and danger trade-off using the Lagrange multiplier method.
We can see that, even though η 0 is completely random, the P-MDP constructed from η 0 generates safe policies when the threat threshold x is small.
When the baseline
[Figure]
policy becomes better, the reward tends to become higher and is enough to obtain near- policy for this problem.
bound
Theorem our theory
4. H IGH -D IMENSIONAL E NVIRONMENTS
For high-dimensional environments, we consider three tasks on 2-D ﬁelds.
See Fig.
1 and Fig.for visualizations.
For Point Gather, the agent’s goal is to collect as many apples as possible while avoiding bombs.
For Circuit, the agent’s goal is to complete one lap around the circuit without crashing into a wall.
The agent can control its movement by regulating its acceleration and steering.
LiDAR sensors are used to compute the distance to obstacles.
For Jam, the LiDAR-equipped agent’s goal is to navigate its way out of a room from the exit located at the top right corner as quickly as possible without bumping into 8 randomly moving objects.
The observation of each environment consists of 29, 364, 392 real values respectively and an agent needs to deal with stochasticity in Jam.
For further details, please see Appendix F. Before explaining the experimental results, we would like to note that the total reward changes depending on how much we severely penalizes the agent for each crash.
As a reference, we show the results when imposing heavier penalty on the crash in
Appendix H.
As we discussed earlier, ﬁnding the optimal policy of the R-MDP is challenging in high-dimensional environments.
However, since the constraint in these environments is to avoid collision with any obstacles, we can use the approximation method introduced in Sec 3.1 that uses a set of sub-R-MDPs containing one obstacle and one agent each.
Thus, to guarantee the safety in P-MDP, we only learned a threat function of a heuristic baseline policy η ( o ) in a sub-R-MDP with one obstacle.
For Circuit and Jam, we treated wall as a set of immobile obstacles so that we can construct the threat function of any shape.
After about 10 minutes of data collection with the generative model, this threat function could be learned in less than half an hour without using GPUs.
Please see the supplemental material for more detailed conditions and videos of the agent trained by each method.
Fig.
3(c),(d) are the heat maps for the upper bound of the threat function computed in the way of Theorem 5.
The color of each heat map at pixel z represents (cid:80) n T η,n( s ( z ) , a ) , where s ( z ) represents the state at which the agent’s current location is z and its velocity and direction is given by the picture located at the left corner of the heat map.
We see that our threat function is playing a role similar to the artiﬁcial potential ﬁeld (Ge & Cui, 2000; Cetin et al., 2011; Lam et al., 2010).
Because our threat function is computed using all aspects of the agent’s state (acceleration, velocity, location), we can provide more comprehensive measure of risk compared to other risk metrics such as TTC (Time To Collision)
(Lee, 1976) used in smart automobiles that considers only 1D movement.
Fig.
4 plots the average reward and the crash rate against the training iteration for various methods: (i) classical MPC (exhaustive search), (ii) DQN with Lagrange penalty, and (iii) Constrained Policy
Optimization (CPO) (Achiam et al., 2017a).
DQN and CPO are model-free algorithms, while RP and
MPC are generative model-based algorithms.
(ii) is essentially the same as the Lagrangian-based methods like (Geibel & Wysotzki, 2005; Geibel, 2006).
The results of DQN can be considered as a reference of the difﬁculty of solving CMDP with model-free RL methods.
To compute the threat in
[Figure]
[Table]
[Figure]
MPC, we took the same strategy of threat decomposition as our method G.4 in order to make the prediction
The average values and the 90% conﬁdence intervals in the plot were computed over 10 seeds.
The curve plotted for our approx.
RP corresponds to the result obtained by a DQN-based P-MDP solution.
The plot does not include the R-MDP phase.
As we can see, our method achieves the highest reward at almost all phases of the training for both Circuit and Jam, while maintaining the lowest crash rate.
Our method is safer than the 3-step MPC for both Jam and Circuit as well, a method with signiﬁcantly higher runtime computational cost.
For point-gather, RP with x = 0 .
1 performs worse in terms of reward than the penarlized DQN and CPO.
Although 0 . 1 is a value that is slightly larger than the minimum safety threshold that guarantees the safety, our RP with x = 0 .
1 performs very conservatively in term of safety.
This suggest that the pool of actions allowed for x = 0 .
1 is probably still too small.
Fortunately, however, we can ﬁne-tune the threshold to increase the pool of allowed actions for P-MDP without re-learning the threat function.
Our RP with x = 0 .
5 performs competitively while satisfying the prescribed constraint.
That we can ﬁne-tune the threshold without retraining is a signiﬁcant advantage of our method over Lagrange-type methods.
Also, by the token of Corollary 3, that our policy is experimentally safe suggests that greedy- η ( o ) is also safe for the choice of the baseline policy η ( o ) .
4. R ECYCLING OF THE THREAT FUNCTION FOR A CMDP IN DIFFERENT ENVIRONMENT
As we mentioned in introduction, we can reuse the threat function for different CMDP tasks with different goals if their safety constraints are deﬁned similarly.
In order to verify this powerful feature of our algorithm, we conducted two sets of experiments in which we apply a policy learned on one environment to the tasks on another environment.
For the ﬁrst set of experiments, we trained a safe
[Table]
[Table]
evaluated its performance on the environments that are different from the original circuit used in the training, (1) narrowed circuit with original shape, and (2) differently shaped circuit with same width.
For the second set of experiments, we trained a safe policy for the Jam task, and tested its performance on other Jam tasks with different numbers of randomly moving obstacles.
Figs.
1 and 2 show the results.
For the modiﬁed Jam, we have no results for MPC with more than 3-step prediction since the exhaustive search cannot be completed within reasonable time-frame.
The 4-step MPC requires 18.2secs per each 100 steps for Circuit, and the 3-step MPC requires 285secs per each 100 steps for the original Jam.
We ﬁnd that, even in varying environments, the policy obtained by our method can guarantee safety with high probability while seeking high reward.
On the other hand, DQN in new environment is failing both in terms of reward and safety.
C ONCLUSION
In this study we proposed a method that isolates the safety seeking procedure from reward-seeking procedure in solving CMDP.
Although our method does not guarantee to ﬁnd the optimal rewardseeking safe policy, it can perform signiﬁcantly better than classical methods both in terms of safety and reward in high-dimensional dynamic environments like Jam.
Our method is designed so that more training in R-MDP will always help increase the search space in P-MDP.
Our treatment of the threat function not only allows us to solve similar problems without the need of re-learning, it also helps us obtain more comprehensive measure of danger at each state than conventional methods.
Overall, we ﬁnd that utilizing threat functions is a promising approach to safe RL and further research on framework may lead to new CMDP methods applicable to complex, real-world environments.
A PPENDIX A P ROOF OF T HEOREM
Proof.
Let’s ﬁrst prove the theorem by induction.
At t =
T , Eq.
(6) holds because both sides are zero.
Suppose Eq
(6) holds at t + 1 .
Then at time t the danger value function of π at t satisﬁes
[Equation]
The above inequality follows from the induction hypothesis at t + 1 , and the last equality is from the deﬁnition of T ηt and the property of conditional expectation.
Since π ∈ Π η, x G , when s t / ∈ S
η, x we know that E π (cid:104) T ηt ( s t , a t ) | s t (cid:105) ≤ E η [ T ηt ( s t , a t ) | s t ] .
Noting the fact above and D ηt ( s t ) =
E η [ T ηt ( s t , a t ) | s t ] , for the ﬁrst term in Eq.
(10), we have
[Equation]
Here the ﬁrst inequality holds because the term is negative when s t / ∈ S
η, x .
The second inequality is true by taking the absolute value with fact that T ηt ( s t , a t ) ≥ 0 .
The thirdinequality follows from the property of the total variation distance.
The last inequality is true because of the construction of Π η, x G .
Now we get
[Equation]
which completes the induction step and the proof of Theorem 1.Notice that the logic used here is essentially same as the one used in the proof of Theorem 1 of (Achiam et al., 2017a).
However, our result is slightly different in that we consider ﬁnite time-horizon and we utilize our assumption that T ηt ( s, a ) ≤ x t for ( η, x ) -secure states.
B P ROOF OF C OROLLARY
Proof.
First, we will derive a result similar to Theorem 1.
Theorem 2.
For a given policy η and a sequence of safety thresholds x
= ( x 0 , . . .
, x T − 1 ) , let π be a policy in Π η, x .
Then for all t ∈ { 0 , . .
, T − 1 } ,
[Equation]
13) where z gt is a distance measure between π and greedy- η given by
[Equation]
Let’s prove the theorem by induction.
At t =
T , Eq.
(13) holds because both sides are zero.
Suppose
Eq.
(13) holds at t + 1 .
Then at time t , the danger value function of π at t satisﬁes
[Equation]
The above inequality follows from the induction hypothesis at t + 1 , and the last equality is from the deﬁnition of T greedy- η t and the property of conditional expectation.
Since π ∈ Π η, x , when s t / ∈ S η, x we know that π ( ·| s t )
= greedy-
η ( ·| s t ) .
Noting the fact above and
D greedy- η t ( s
[Equation]
= E greedy-
[Equation]
[ T greedy- η t ( s t , a t ) | s t ] , for the ﬁrst term in Eq.
(14), we have
[Equation] [Equation]
Here the ﬁrst inequality is true by taking the absolute value with fact that T greedy- η t ( s t , a t ) ≥ 0 .
The second inequality follows from the property of the total variation distance.
The third inequality holds because greedy- η is the policy after one step of policy improvement from η , hence T greedy- η t ( s t , a ) ≤ T ηt ( s t , a ) .
The last inequality is true because of the construction of Π η, x .
Now we get
[Equation]
which completes the induction step and the proof of Theorem 2.
Now using Theorem 2, Corollary 3 follows using the same arguments as Corollary 2.
C P ROOF OF T HEOREM
By the assumption about the transition probability,
[Equation]
Using this property, the claim of Theorem 5 immediately follows by applying the union bound d ( s t , a t ) ≤ (cid:80)
Nn =1 d n ( s ot , s nt , a t ) .
D A NOTHER INTERPRETATION FOR P-MDP
Since we restrict attention to policies in Π η , the P-MDP can be viewed as an MDP deﬁned of the smaller state space S η .
Namely, we constructed the tuple ( S η , A η , r ηP , P ηP , P 0 ) , whose components are deﬁned as follows.
The function r ηP ( ·| s ) is the restriction of the reward r to A η ( s ) for all s ∈ S η .
P ηP is a transition probability function derived from the original state transition probability P such that, for all s 1 , s 2 ∈ S η , P ηP ( s 2 | s 1 , a ) =
P ( s 2 | s 1 , a ) + P (( s 1 , a ) (
S η ) c −→ s 2 ) where ( s 1 , a ) ( S η )
c −→ s 2 is the set of all trajectories from s 1 to s 2 that (1) take a detour to ( S η ) c at least once after taking the action a at s 1 , (2) take the action a ∗ ( s (cid:48) )
=
arg min a ∈ A T η ( s (cid:48) , a ) for all s (cid:48) ∈ ( S η ) c , and (3) lead to s 2 without visiting any other states in S η .
E V ALUE ITERATION AND Q- LEARNING FOR P-MDP
When the transition probabilities are available, we can solve the P-MDP using value iteration.
The value iteration for P-MDP can be described by the following algorithm.
When the transition probabilities are not available, we can solve the P-MDP using Q-learning or its
Deep RL versions.
F E NVIRONMENTS C IRCUIT
In this task, the agent’s goal is to complete one lap around the circuit without crashing into the wall.
Each state in the system was set to be the tuple of (1) location, (2) velocity, (3) the direction of the movement, and (4) the raw LiDAR input for each one degree.
Thus, the state observation has 364 real values.
The set of actions allowed for the agent was { 0.15rad to left, 0.05rad to left, stay course, 0.
rad to right, 0.15 rad to right } × { 0.02 unit acceleration, no acceleration, 0.02 unit deceleration } (15 options).
At all time, the speed of the agent was truncated at {− 0 . 04 , 0 . 1 } .
We reward the agent for the distance of travel along the course in the right direction, which accumulates to 1250 pts for one lap.
We also give negative rewards for the stopping and collision at the time step.
We penalize the agent 1 pt for stopping, and penalized the agent by 200 pts for colliding.
We set the length of the episode to 200 steps, which is the approximate number of steps required to make one lap.
In the computation of each summand in the threat decomposition, we augmented the generated dataset by ﬂipping the state with respect to x − axis (left-right).
The constraint for CMDP was set to 1e-3.
J AM
In this task, the agent’s goal is to navigate its way out of a room from the exit located at the top left corner without bumping into 8 randomly moving objects.
We set three circular safety zones centered at each corner except for the top left corner, i.e., exit.
Any moving obstacle entered into the safety zone disappear.
Without the safety zone, the task seems to be too difﬁcult, i.e., there is a situation that the agent cannot avoid the collision even if the agent tries his best.
We set the safety zone to ease the problem hoping the probability that the agent can solve the task when employing the optimal policy becomes reasonably high.
The ﬁeld was 3 × 3 square and the radius of the safety zone located at three corners were set to 0.5.
The radius of the agent and moving obstacles were set to 0.1.
We reward the agent for its distance from the exit, and controlled its value so that the accumulated reward at the goal will be around 85 .
The agent is given 10 points when it reaches the goal, penalized − 0 .points for stopping the advance, and given 50 pts penalty for each collision.
Similar to the setting as in Circuit, the agent is allowed to change direction and acceleration of the movement simultaneously at each time point.
The observation of agent has the information of relative location, relative velocity, and relative direction of 8 nearest cars in addition to the observation of Circuit.
It is composed of totally 396 real values.
The set of actions allowed for the agent is { 0.30 rad to left, 0.10 rad to left, stay course, 0.10 rad to right, 0.30 rad to right } × { 0.02 unit acceleration, no acceleration, 0.02 unit deceleration
P OINT G ATHER
In this task, the goal is to collect as many green apples as possible while avoiding red bombs.
There are 2 apples and 8 bombs in the ﬁeld.
We used the exact same task setting as the one used in the original paper, and the agent is rewarded 10pts when the agent collected apple.
The point mass agent receives 29-dimensional state and can take two-dimensional continuous actions.
For the implementation of DQN, we discretize each action variable into 9 values.
The state variable takes real values, which include position, velocity, direction, distance to the bomb, and etc.
The action variables determine the direction and the velocity of the agent.
As is done in the experiment in (Achiam et al., 2017a), we set the length of each episode to 15 steps.
At every step, our selected baseline policy randomly chooses a direction from [ − 14 .
33 , 14 . 33] degree range with uniform distribution, and move forward with probability 0.9 and move backward with probability 0.1.
After deciding the direction of the move, the baseline policy prompts the agent to move at at the speed chosen uniformly from [0 , 1] unit range.
For the approximation of threat function, we sampled 1 e 6 trajectories by simualtors, and we did not bother to the compute average value for each ﬁxed initial state.
The constraint for
CMDP was set to 0.1.
G M ODEL ARCHITECTURE AND OPTIMIZATION DETAILS G. RP- ALGORITHM
We implement our method on Chainer (Tokui et al., 2015).
The code is available in supplementary material.
L EARNING OF T HREAT FUNCTION IN R-MDP
For the Circuit and Jam tasks, the agent must avoid collisions with both moving obstacles and the wall.
For these environments, it is computationally difﬁcult to obtain the best η .
Thus, we follow the approximate RP algorithm by training a threat function for a baseline policy η ( o ) .
We used η ( o ) that (1) decides the direction of the movement by staying course with probability 0 .
6 , turning right with probability 0 .
2 and turning left with probability 0 .
2 , and (2) decides its speed by accelerating with probability 0 .
2 and decelerating with probability 0 .
We chose not to train η speciﬁcally for this experiment, because η to be used in the decomposition must not depend on the state of all but one obstacle, and we could not expect to improve its safety much by training it.
The threat function for the collision with the immobile point is used to avoid collision with the wall, which can be considered as a set of immobile points.
We thus trained two threat functions in total: one corresponding to the collision with an immobile point, and one corresponding to the collision with a randomly moving obstacle.
Threat function for η ( o ) can be trained by using generative models starting at various initial points to collect samples and by conducting supervised learning.
For the threat function for the collision with immobile object, we used a neural network with three fully connected layers (100-50-15).
For the threat function for the collision with moving obstacles, we used a network with four fully connected layers (500-500-500-15).
For the training dataset, we sampled 100,000 initial state and simulated 10,000 paths of length 5 from each initial state.
We train the network with Adam.
The parameter settings for the training of the threat function of immobile point and of mobile obstacle are ( α = 1 e − 2 , (cid:15) = 1 e − 2 , batchsize = 512, number of epochs = 20), and ( α = 1 e − 3 , (cid:15) = 1 e − 2 , batchsize = 512, number of epochs = 25), respectively.
We used relu as the activation function for the approximation of all threat functions.
For the Point Gather task, we again use the approximate RP algorithm.
The threat function is estimated by a three-layer (100-100-81) fully connected neural network.
S OLVING P-MDP
For the Planning MDP, we use DQN.
The network used for the Q-function in P-MDP looks like Φ( (cid:96), s ) , where (cid:96) is the result of applying lider outputs to 1D conv network, s is the combination of location, velocity and angle, and Φ is a three-layered fully connected network (50-20-15) with tanh activation functions.
The convolutional layer has 3 output channels with kernel size = 6 and stride = 1, followed by max pooling with kernel size = 8.
In Point Gather, we use fully connected neural network with three layers (50-50-81).
We trained the network with Adam ( α = 1 e − 3 , (cid:15) = 1 e − 2 ).
We linearly decayed ε for ε -greedy from 1 to 0 .
05 over 3M iterations.
We used one GPU, with which it took no longer than about 24 hours to complete 3M iterations.
G. DQN WITH L AGRANGE COEFFICIENT
As for the network, we used the same DQN architecture as the one we used in P-MDP.
We used tanh for the activation function.
For Lagrange coefﬁcient, we test with three Lagrange coefﬁcients for each task, { 0 , 20 , 200 } for Circuit, { 5 , 50 , 500 } for Jam, { 1 , 10 , 50 } for Point Gather, respectively.
For the Jam task, the initial Lagrange coefﬁcients are all set to 5 and gradually incremented to the ﬁnal values { 1 , 10 , 50 } as done in (Miyashita et al., 2018).
This heuristic pushes the agent to learn the goal-oriented policy ﬁrst, and then learn the collision avoidance.
G. C ONSTRAINED P OLICY O PTIMIZATION
As for CPO, we use the same implementation publicized on Github (Achiam et al., 2017b), i.e., the policy is a Gaussian policy implemented by a three layer MLP with hidden units 64 and 32 for all tasks.
We used tanh for the activation function.
G. M ODEL P REDICTIVE C ONTROL
We tested MPC with receding horizon ∈ { 3 , 4 , 5 } for Circuit, ∈ { 2 , 3 } for Jam and ∈ { 1 , 2 } for Point
Gather.
At every step, selected the action with the highest reward among those that were deemed safe by the prediction.
To select the next safe action, we used tree-search on the sequence of actions, and prematurely pruned branches that were sure to violate the safety condition.
Also, for the JAM experiment, we used the same strategy as our approximate-RP.
More particularly, we allowed MPC to make predictions in a set of environments consisting of one pair of obstacle and ego-object each, and evaluated the safety by summing the danger values computed from all environments.
This was done in order to reduce the computation time of prediction (which already is large), because the number of combinatorial cases explodes as we increase the number of obstacle objects.
H P ERFORMANCE EVALUATIONS WITH LARGER COLLISION PENALTY ON C IRCUIT AND J AM
Here we show the score computed with a larger collision penalty on Circuit and Jam tasks to stress the fact that the performance will vary arbitrarily depending on how much we put emphasis on the constraints.
In below, the collision penalty is increased to be 1500pts and 500pts instead of the ones used in Tables1 and 2 where the collision penalties are 200pts and 50pts, respectively.
[Table]
Time stands for the computation time consumed in making 100 action steps.
Note that our method never crashes in this experiment.
In this experiment, every crash is penalized by 1500pts.
[Table]
I A DDITIONAL RELATED WORK
As mentioned in the introduction, one classic strategy for safe maximization of the reward is MPC, which uses a simulator/generator to directly predict the outcome of candidate actions.
There are many applications and variations of this approach (Maciejowski, 2002; Yoshida et al., 2008; Wang &
Boyd, 2010; Di Cairano et al., 2013; Liu & Jr, 2014; Weiskircher et al., 2017; Rasekhipour et al., 2016b).
One straight forward approach for constrained Markov Decision process is to formulate the objective with Lagrange multiplier.
This approach has been used widely in practice, including (Geibel & Wysotzki, 2005; Geibel, 2006; Uchibe & Doya, 2007; Everett et al., 2018; Walraven &
Spaan, 2018; Paternain et al., 2019) and CPO(Achiam et al., 2017a).
CPO is a method that gradually improves the safe policy by making a local search for a better safe policy in the neighborhood of the current safe policy.
By nature, at each update, CPO has to determine whether a suggested member in the neighborhood of current safe policy satisﬁes the safety constraint.
In implementation, this is again done by the evaluation of Lagrange coefﬁcients.
Accurate selection of safe policies in the neighborhood is especially difﬁcult when the danger to be avoided is "rare" and "catastrophic", because we would need massive number of samples to verify whether a given policy is safe or not.
This is a problem common to all approaches using the Lagrange multipliers, because the optimization of Lagrange coefﬁcient requires accurate evaluation of the expected danger.
Moreover, because each update is incremental, we have to repeat this process multiple times (usually, several dozens of times for Point Circle, and several thousands of times for Ant Gather and Humanoid Circle).
Lyapunov based approach (Chow et al., 2018; 2019) are also similar in nature.
At each step of the algorithm,
Lyapunov based approach uses safety-margin function (cid:15) —a state-dependent measure of how bold the agent can be while remaining safe– to construct a set of safe policies in the neighborhood of the baseline policy.
For the accurate computation of the margin, one must use the transition probability to solve a linear programming problem in the space whose dimension equals the number of states in the system.
This computation requires O ( | X ||
A | 2 ( | A | + 2)) computation time.
Especially when dealing with system with rare dangerous events, the set of policies found this way may still contain unsafe policies.
Reachability analysis is a theory that provides a safety guarantee.
However, they built their theory on more restrictive setting than the one we consider here (Takei et al.
; Ding et al.
; Summers et al.
; Rasekhipour et al., 2016a).
Model-checking is another approach to guarantee the safety.
Once the constraints are represented in the form of temporal logic constraints or computation-tree logic (Baier et al., 2003; Wen et al., 2015; Bouton et al., 2019), we can ensure the safety by using model checking systems.
However, it is sometimes difﬁcult to express the constraints in such a structured form.
Also, even when we can represent the constraints in the structured form, the computational cost can be heavy.
When the state-action space is large, the computation time required for the model-checking system can become prohibitively heavy.