Truly Deterministic Policy Optimization

In this paper , we present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape . By avoiding noise injection all sources of estimation variance can be eliminated in systems with deterministic dynamics ( up to the initial state distribution ) . Since deterministic policy regularization is impossible using traditional non-metric measures such as the KL divergence , we derive a Wasserstein-based quadratic model for our purposes . We state conditions on the system model under which it is possible to establish a monotonic policy improvement guarantee , propose a surrogate function for policy gradient estimation , and show that it is possible to compute exact advantage estimates if both the state transition model and the policy are deterministic . Finally , we describe two novel robotic control environments—one with non-local rewards in the frequency domain and the other with a long horizon ( 8000 time-steps ) — for which our policy gradient method ( TDPO ) significantly outperforms existing methods ( PPO , TRPO , DDPG , and TD3 ) . Policy Gradient ( PG ) methods can be broadly characterized by three defining elements : the policy gradient estimator , the regularization measures , and the exploration profile . For gradient estimation , episodic ( Williams , 1992 ) , importance-sampling-based ( Schulman et al. , 2015a ) , and deterministic ( Silver et al. , 2014 ) gradients are some of the most common estimation oracles . As for regularization measures , either an Euclidean distance within the parameter space ( Williams , 1992 ; Silver et al. , 2014 ; Lillicrap et al. , 2015 ) , or dimensionally consistent non-metric measures ( Schulman et al. , 2015a ; Kakade & Langford , 2002 ; Schulman et al. , 2017 ; Kakade , 2002 ; Wu et al. , 2017 ) have been frequently adapted . Common exploration profiles include Gaussian ( Schulman et al. , 2015a ) and stochastic processes ( Lillicrap et al. , 2015 ) . These elements form the basis of many model-free and stochastic policy optimization methods successfully capable of learning high-dimensional policy parameters . Both stochastic and deterministic policy search can be useful in applications . A stochastic policy has the effect of smoothing or filtering the policy landscape , which is desirable for optimization . Searching through stochastic policies has enabled the effective control of challenging environments under a general framework ( Schulman et al. , 2015a ; 2017 ) . The same method could either learn robotic movements or play basic games ( 1 ) with minimal domain-specific knowledge , ( 2 ) regardless of function approximation classes , and ( 3 ) with less human intervention ( ignoring reward engineering and hyper-parameter tuning ) ( Duan et al. , 2016 ) . Using stochasticity for exploration , although it imposes approximations and variance , has provided a robust way to actively search for higher rewards . Despite many successes , there are practical environments which remain challenging for current policy gradient methods . For example , non-local rewards ( e.g. , those defined in the frequency domain ) , long time horizons , and naturally-resonant environments all occur in realistic robotic systems ( Kuo & Golnaraghi , 2002 ; Meirovitch , 1975 ; Preumont & Seto , 2008 ) but can present issues for policy gradient search . To tackle challenging environments such as these , this paper considers policy gradient methods based on deterministic policies and deterministic gradient estimates , which could offer advantages by allowing the estimation of global reward gradients on long horizons without the need to inject noise into the system for exploration . To facilitate a dimensionally consistent and low-variance deterministic policy search , a compatible policy gradient estimator and a metric measure for regularization should be employed . For gradient estimation we focus on Vine estimators ( Schulman et al. , 2015a ) , which can be easily applied to deterministic policies . As a metric measure we use the Wasserstein distance , which can measure meaningful distances between deterministic policy functions that have non- overlapping supports ( in contrast to the Kullback-Liebler ( KL ) divergence and the Total Variation ( TV ) distance ) . The Wasserstein metric has seen substantial recent application in a variety of machine-learning domains , such as the successful stable learning of generative adversarial models ( Arjovsky et al. , 2017 ) . Theoretically , this metric has been studied in the context of Lipschitz-continuous Markov decision processes in reinforcement learning ( Hinderer , 2005 ; Ferns et al. , 2012 ) . Pirotta et al . ( 2015 ) defined a policy gradient method using the Wasserestein distance by relying on Lipschitz continuity assumptions with respect to the policy gradient itself . Furthermore , for Lipschitz-continuous Markov decision processes , Asadi et al . ( 2018 ) and Rachelson & Lagoudakis ( 2010 ) used the Wasserstein distance to derive model-based value-iteration and policy-iteration methods , respectively . On a more practical note , Pacchiano et al . ( 2019 ) utilized Wasserstein regularization for behavior-guided stochastic policy optimization . Moreover , Abdullah et al . ( 2019 ) has proposed another robust stochastic policy gradient formulation . Estimating the Wasserstein distance for general distributions is more complicated than typical KL-divergences ( Villani , 2008 ) . This fact constitutes and emphasizes the contributions of Abdullah et al . ( 2019 ) and Pacchiano et al . ( 2019 ) . However , for our deterministic observation-conditional policies , closed-form computation of Wasserstein distances is possible without any approximation . Existing deterministic policy gradient methods ( e.g. , DDPG and TD3 ) use deterministic policies ( Silver et al. , 2014 ; Lillicrap et al. , 2015 ; Fujimoto et al. , 2018 ) , meaning that they learn a deterministic policy function from states to actions . However , such methods still use stochastic search ( i.e. , they add stochastic noise to their deterministic actions to force exploration during policy search ) . In contrast , we will be interested in a method which not only uses deterministic policies , but also uses deterministic search ( i.e. , without constant stochastic noise injection ) . We call this new method truly deterministic policy optimization ( TDPO ) and it may have lower estimation variances and better scalability to long horizons , as we will show in numerical examples . Scalability to long horizons is one of the most challenging aspects for policy gradient methods that use stochastic search . This issue is sometimes referred to as the curse of horizon in reinforcement learning ( Liu et al. , 2018 ) . General worst-case analyses suggests that the sample complexity of reinforcement learning is exponential with respect to the horizon length ( Kakade et al. , 2003 ; Kearns et al. , 2000 ; 2002 ) . Deriving polynomial lower-bounds for the sample complexity of reinforcement learning methods is still an open problem ( Jiang & Agarwal , 2018 ) . Lower-bounding the sample complexity of reinforcement learning for long horizons under different settings and simplifying assumptions has been a topic of theoretical research ( Dann & Brunskill , 2015 ; Wang et al. , 2020 ) . Some recent work has examined the scalability of importance sampling gradient estimators to long horizons in terms of both theoretical and practical estimator variances ( Liu et al. , 2018 ; Kallus & Uehara , 2019 ; 2020 ) . All in all , long horizons are challenging for all reinforcement learning methods , especially the ones suffering from excessive estimation variance due to the use of stochastic policies for exploration , and our truly deterministic method may have advantages in this respect . In this paper we focus on continuous-domain robotic environments with reset capability to previously visited states . The main contributions of this work are : ( 1 ) we introduce a Deterministic Vine ( DeVine ) policy gradient estimator which avoids constant exploratory noise injection ; ( 2 ) we derive a novel deterministically-compatible surrogate function and provide monotonic payoff improvement guarantees ; ( 3 ) we show how to use the DeVine policy gradient estimator with the Wasserstein-based surrogate in a practical algorithm ( TDPO : Truly Deterministic Policy Optimization ) ; ( 4 ) we illustrate the robustness of the TDPO policy search process in robotic control environments with non-local rewards , long horizons , and/or resonant frequencies . 1 BACKGROUND . MDP preliminaries . An infinite-horizon discounted Markov decision process ( MDP ) is specified by ( S , A , P , R , µ , γ ) , where S is the state space , A is the action space , P : S × A → ∆ ( S ) is the transition dynamics , R : S × A → [ 0 , Rmax ] is the reward function , γ ∈ [ 0 , 1 ) is the discount factor , and µ ( s ) is the initial state distribution of interest ( where ∆ ( F ) denotes the set of all probability distributions over F , otherwise known as the Credal set of F ) . The transition dynamics P is defined as an operator which produces a distribution over the state space for the next state s′ ∼ P ( s , a ) . The transition dynamics can be easily generalized to take distributions of states or actions as input ( i.e. , by having P defined as P : ∆ ( S ) ×A → ∆ ( S ) or P : S ×∆ ( A ) → ∆ ( S ) ) . We may abuse the notation and replace δs and δa by s and a , where δs and δa are the deterministic distributions concentrated at the state s and action a , respectively . A policy π : S → ∆ ( A ) specifies a distribution over actions for each state , and induces trajectories from a given starting state s as follows : s1 = s , a1 ∼ π ( s1 ) , r1 = R ( s1 , a1 ) , s2 ∼ P ( s2 , a2 ) , a2 ∼ π ( s2 ) , etc . We will denote trajectories as state-action tuples τ = ( s1 , a1 , s2 , a2 , . . . ) . One can generalize the dynamics ( 1 ) by using a policy instead of an action distribution P ( µs , π ) : = Es∼µs [ Ea∼π ( s ) [ P ( s , a ) ] ] , and ( 2 ) by introducing the t-step transition dynamics recursively as Pt ( µs , π ) : = P ( Pt−1 ( µs , π ) , π ) with P0 ( µs , π ) : = µs , where µs is a distribution over S. The visitation frequency can then be defined as ρπµ : = ( 1− γ ) ∑∞ t=1 γ t−1Pt−1 ( µ , π ) . Table 2 of the appendix summarizes all MDP notation . The value function of π is defined as V π ( s ) : = E [ ∑∞ t=1 γ t−1rt | s1 = s ; π ] . Similarly , one can define Qπ ( s , a ) by conditioning on the first action . The advantage function can then be defined as their difference ( i.e . Aπ ( s , a ) : = Qπ ( s , a ) − V π ( s ) ) . Generally , one can define the advantage/value of one policy with respect to another using Aπ ( s , π′ ) : = E [ Qπ ( s , a ) − V π ( s ) | a ∼ π′ ( ·|s ) ] and Qπ ( s , π′ ) : = E [ Qπ ( s , a ) | a ∼ π′ ( ·|s ) ] . Finally , the payoff of a policy ηπ : = E [ V π ( s ) ; s ∼ µ ] is the average value over the initial states distribution of the MDP . Probabilistic and mathematical notations . Sometimes we refer to ∫ f ( x ) g ( x ) dx integrals as 〈f , g〉x Hilbert space inner products . Assuming that ζ and ν are two probabilistic densities , the Kulback-Liebler ( KL ) divergence is DKL ( ζ|ν ) : = 〈ζ ( x ) , log ( ζ ( x ) ν ( x ) ) 〉x , the Total-Variation ( TV ) distance is TV ( ζ , ν ) = : 12 〈|ζ ( x ) − ν ( x ) | , 1〉x , and the Wasserstein distance is W ( ζ , ν ) = infγ∈Γ ( ζ , ν ) 〈‖x − y‖ , γ ( x , y ) 〉x , y where Γ ( ζ , ν ) is the set of couplings for ζ and ν . We define Lip ( f ( x , y ) ; x ) : = supx ‖∇xf ( x , y ) ‖2 and assume the existence of Lip ( Qπ ( s , a ) ; a ) and ‖Lip ( ∇sQπ ( s , a ) ; a ) ‖2 constants . Under this notation , the Rubinstein-Kantrovich ( RK ) duality states that the |〈ζ ( x ) − ν ( x ) , f ( x ) 〉x| ≤W ( ζ , ν ) · Lip ( f ; x ) bound is tight for all f . For brevity , we may abuse the notation and denote supsW ( π1 ( ·|s ) , π2 ( ·|s ) ) with W ( π1 , π2 ) ( and similarly for other measures ) . For parameterized policies , we define∇πf ( π ) : = ∇θf ( π ) where π is parameterized by the vector θ . Table 1 of the appendix summarizes all these mathematical definitions . Policy gradient preliminaries . The advantage decomposition lemma provides insight into the relationship between payoff improvements and advantages ( Kakade & Langford , 2002 ) . That is , ηπ2 − ηπ1 = 1 1− γ · Es∼ρπ2µ [ A π1 ( s , π2 ) ] . ( 1 ) We will denote the current and the candidate next policy as π1 and π2 , respectively . Taking derivatives of both sides with respect to π2 at π1 yields ∇π2ηπ2 = 1 1− γ [ 〈∇π2ρπ2µ ( · ) , Aπ1 ( · , π1 ) 〉+ 〈ρπ1µ ( · ) , ∇π2Aπ1 ( · , π2 ) 〉 ] . ( 2 ) Since π1 does not have any advantage over itself ( i.e. , Aπ1 ( · , π1 ) = 0 ) , the first term is zero . Thus , the Policy Gradient ( PG ) theorem is derived as ∇π2ηπ2 ∣∣∣ π2=π1 = 1 1− γ · Es∼ρπ1µ [ ∇π2A π1 ( s , π2 ) ] ∣∣∣ π2=π1 . ( 3 ) For policy iteration with function approximation , we assume π2 and π1 to be parameterized by θ2 and θ1 , respectively . One can view the PG theorem as a Taylor expansion of the payoff at θ1 . Conservative Policy Iteration ( CPI ) ( Kakade & Langford , 2002 ) was one of the early dimensionally consistent methods with a surrogate of the form Lπ1 ( π2 ) = ηπ1 + 11−γ · Es∼ρπ1µ [ A π1 ( s , π2 ) ] − C 2 TV 2 ( π1 , π2 ) . The C coefficient guarantees non-decreasing payoffs . However , CPI is limited to linear function approximation classes due to the update rule πnew ← ( 1− α ) πold + απ′ . This lead to the design of the Trust Region Policy Optimization ( TRPO ) ( Schulman et al. , 2015a ) algorithm . TRPO exchanged the bounded squared TV distance with the KL divergence by lower bounding it using the Pinsker inequality . This made TRPO closer to the Natural Policy Gradient algorithm ( Kakade , 2002 ) , and for Gaussian policies the modified terms had similar Taylor expansions within small trust regions . Confined trust regions are a stable way of making large updates and avoiding pessimistic coefficients . For gradient estimates , TRPO used Importance Sampling ( IS ) with a baseline shift : ∇θ2Es∼ρπ1µ [ A π1 ( s , π2 ) ] ∣∣∣ θ2=θ1 = ∇θ2E s∼ρπ1µ a∼π1 ( ·|s ) [ Qπ1 ( s , a ) π2 ( a|s ) π1 ( a|s ) ] ∣∣∣ θ2=θ1 . ( 4 ) While empirical E [ Aπ1 ( s , π2 ) ] and E [ Qπ1 ( s , π2 ) ] estimates yield identical variances in principle , the importance sampling estimator in ( 4 ) imposes larger variances . Later , Proximal Policy Optimization ( PPO ) ( Schulman et al. , 2015b ) proposed utilizing the Generalized Advantage Estimation ( GAE ) method for variance reduction and incorporated first-order smoothing like ADAM ( Kingma & Ba , 2014 ) . GAE employed Temporal-Difference ( TD ) learning ( Bhatnagar et al. , 2009 ) for variance reduction . Although TD-learning was not theoretically guaranteed to converge and could add bias , it improved the estimation accuracy . As an alternative to importance sampling , deterministic policy gradient estimators were also utilized in an actor-critic fashion . Deep Deterministic Policy Gradient ( DDPG ) ( Lillicrap et al. , 2015 ) generalized deterministic gradients by employing Approximate Dynamic Programming ( ADP ) ( Mnih et al. , 2015 ) for variance reduction . Twin Delayed Deterministic Policy Gradient ( TD3 ) ( Fujimoto et al. , 2018 ) improved DDPG ’ s approximation to build an even better policy optimization method . Although both methods used deterministic policies , they still performed stochastic search by adding stochastic noise to the deterministic policies to force exploration . Other lines of stochastic policy optimization were later proposed . Wu et al . ( 2017 ) used a Kroneckerfactored approximation for curvatures . Haarnoja et al . ( 2018 ) proposed a maximum entropy actorcritic method for stochastic policy optimization .

Summary: This paper introduces a policy gradient method based on deterministic policies and deterministic gradient estimates. By assuming such a deterministic setting, the authors show that the proposed technique can estimate gradients on long-horizon tasks without the need to inject noise into the system for exploration. The authors propose using a Wasserstein-based model to perform regularization in this particular setting involving deterministic policies. They also establish the conditions under which it is possible to guarantee monotonic policy improvement.