Factored Action Spaces in Deep Reinforcement Learning

1 INTRODUCTION . In many decision making problems , especially for combinatorial problems , the search space can be extremely large . Learning from scratch in this setting can be hard if not sometimes impossible . Using deep neural networks helps dealing with very large state spaces , but the issue remains when the action space or the horizon required to solve a problem are too large , which is often the case in many real-world settings . Several approaches tackle the problem of long horizon tasks like learning compositional neural programs ( Pierrot et al. , 2019 ) , hierarchical policies ( Levy et al. , 2018 ) or options ( Bacon et al. , 2017 ) . However , the large action space problem is not as well covered . The main approach consists in factorizing the action space into a Cartesian product of smaller subspaces . We call it Factored Actions Reinforcement Learning ( FARL ) . In FARL , the agent must return a sequence of actions at each time step instead of a single one . This approach has been applied successfully to obtain astonishing results in games like StarCraft ( Jaderberg et al. , 2019 ) , Dota 2 ( Berner et al. , 2019 ) or for neural program generation ( Li et al. , 2020 ) . There was also several attempts to use factored action spaces with DQN , PPO and AlphaZero to solve continuous action problems by discretizing actions and specifying each dimension at a time ( Metz et al. , 2017 ; Grill et al. , 2020 ; Tang & Agrawal , 2020 ) . The resulting algorithms outperformed several native continuous action algorithms on MUJOCO benchmarks . While this approach has been successfully applied in practice , a deeper analysis of the consequences of such a formulation on the RL problem is missing . In this paper , we highlight two different ways to factorize the policy and study their theoretical impact . We discuss the pros and cons of both approaches and illustrate them with practical applications . We extend two state-of-the-art agents PPO and SAC to work with both factorization methods . To highlight the generality of the approach , we apply these algorithms to diverse domains , from large sequential decision problems with discrete actions to challenging continuous control problems and hybrid domains mixing discrete decisions and continuous parameterization of these decisions . We illustrate the method on three benchmarks chosen for the different difficulties they raise and highlight the benefits of using factored actions . 2 RELATED WORK . A large part of the reinforcement learning literature covers the long time horizon problem with approaches based on options ( Bacon et al. , 2017 ; Vezhnevets et al. , 2017 ) , compositionality ( Pierrot et al. , 2019 ; 2020 ) or more generally Hierarchical Reinforcement Learning ( Levy et al. , 2017 ; 2018 ; Yang et al. , 2018 ; Nachum et al. , 2018a ; b ) . However , there has been fewer attempts to deal with large action spaces . In many real life problems , especially in combinatorial or optimisation research standard problems , the number of entities and the size of instances can be very large thus leading to action spaces which may contain thousands of actions . Some prior works have focused on factorizing the action space into binary sub-spaces and using generalized value functions ( Pazis & Parr , 2011 ) . A similar approach leveraged Error-Correcting Output Code classifiers ( ECOCs ) ( Dietterich & Bakiri , 1994 ) to factorize the action space and allow for parallel training of a sub-policy for each action sub-space ( Dulac-Arnold et al. , 2012 ) . More recently , Dulac-Arnold et al . ( 2015 ) proposed to leverage prior information about the actions to embed them into a continuous action space in which the agent can generalize . A concurrent approach is to learn what not to learn ( Zahavy et al. , 2018 ) . The authors train an action elimination network to eliminate sub-optimal actions , thus reducing the number of possible actions for the RL agent . In another approach , the action space is factored into a Cartesian product of n discrete sub-action spaces . In Parameterized actions RL , also called Hybrid RL , actions are factored into sequences that correspond to the choice of an action in a discrete action space of size m and then the choice of the intensity of this action in a continuous action space ( Hausknecht & Stone , 2015 ; Masson et al. , 2016 ; Fan et al. , 2019 ; Delalleau et al. , 2019 ) . In other problems , the action space exhibits a natural factorization as in Dota 2 or StarCraft . Indeed , one must first choose a macro-action such as selecting a building or a unit and then a sequence of micro-actions such as creating a specific unit , at a specific position . In such a factorization , the autoregressive property is essential , as the selection of an action must be conditioned on the previously selected actions in the sequence . For both games , factorizing the action space and selecting sequences of autoregressive actions instead of single discrete actions has been shown to be crucial ( Berner et al. , 2019 ; Vinyals et al. , 2019 ) . However neither of these works sufficient highlight this aspect nor propose a proper formalisation . As far as we know , the only work that establishes a proper FARL framework is Metz et al . ( 2017 ) with the model called Sequential DQN ( SDQN ) . They build on existing methods to construct sequential models that have been proposed outside the RL literature . Notably , these models are a natural fit for language modelling Bengio et al . ( 2003 ) ; Sutskever et al . ( 2014 ) . Metz et al . ( 2017 ) extend the DQN algorithm ( Mnih et al. , 2013 ) to the sequential setting and present this approach as an alternative way to handle continuous action spaces such as robotic control . Here , we go beyond Q-learning approaches and propose general formulations to extend any actor-critic RL agent to the FARL setting . We illustrate this framework on two examples : we extend both the Proximal Policy Optimization ( PPO ) ( Schulman et al. , 2017 ) and Soft Actor Critic ( SAC ) algorithms ( Haarnoja et al. , 2018 ) to the sequential setting . We also highlight the flexibility and generality of the FARL approach by using it on a broad class of problems . We show results on robotic control MUJOCO benchmarks as in Metz et al . ( 2017 ) to demonstrate the relevance of our derivations , and we also successfully apply factored PPO and SAC to parameterized and multi-agent problems . 3 FACTORED ACTION SPACES . In this section , we introduce notations for Markov Decision Problems with factored action spaces . We consider a Markov Decision Process ( MDP ) ( S , A , T , R , γ , ρ0 ) where S is the state space , A the action space , T : S ×A → S the transition function , R : S ×A× S → R the reward function , γ ≤ 1 is a discount factor and ρ0 is the initial state distribution . We assume that the state space is continuous and that the MDP is fully-observable , thus observations equal states . In this paper , we assume that the action space is factored , thus it might be expressed as a Cartesian product of n discrete action sub-spaces : A = A1 × · · · × An where Ai is a discrete action space of size ni . We aim to learn a parameterized stochastic policy πθ : A× S → [ 0 , 1 ] , where π ( a|s ) is the probability of choosing action a in state s. The objective function to maximise is J ( θ ) = IEτ [ ∑∞ t=0 γ trt ] where τ is a trajectory obtained from πθ starting from state s0 ∼ ρ0 and rt is the reward obtained along this trajectory at time t. We define the Q-value for policy π , Qπ : S × A → R as Qπ ( s , a ) = IEτ [ ∑ t γ trt ] , where τ is a trajectory obtained from πθ starting from state s and performing initial action a . We define the V-value V π : S → R as V ( s ) = ∑ a∈A π ( a|s ) Qπ ( s , a ) . The policy is factored into a product of n joint distributions to handle the factored action space . We consider two settings . Independent Factorization . A first setting corresponds to problems in which the actions components can be chosen independently from each other , only taking into account the environment state s. In this case , we decompose policy πθ into n policies πiθ : S → Ai such that ∀ ( s , a ) ∈ S × A , πθ ( a|s ) = ∏n i=1 π i θ ( a i|s ) where ai is the ith component of action a . Each sub-policy i returns probabilities over the possible actions ai ∈ Ai . In this setting , to sample an action from πθ , we sample in parallel the sub-actions from the sub-distributions πiθ . Autoregressive Factorization . In this second setting , actions are assumed ordered and dependent . For instance , the choice of action a2 depends on the value of the action a1 that has been chosen by policy π1 . To account for this , we impose the autoregressive property , i.e . intermediate actions ai are selected conditionally to previous action choices a1 , . . . , ai−1 . More formally , the probability of choosing action ai is πiθ ( a i|s , a1 , . . . , ai−1 ) . As Metz et al . ( 2017 ) , we introduce sub-state spaces Ui = S × A1 × · · · × Ai where U0 = S and associated sub-states uit ∈ Ui , which contain the information of the environment state st and all the sub-actions that have been selected so far . We decompose the policy π : S → A into n sub-policies πi : Ui−1 → Ai , i ∈ [ 1 , n ] such that ∀ ( s , a ) ∈ S ×A , πθ ( at|st ) = ∏n i=1 π i θ ( a i t|ui−1t ) . In this setting the sub-actions can not be sampled in parallel . To sample an action from πθ , we sample each sub-action sequentially , starting from the first and conditioning each sub-policy on the previously sampled actions . 4 PROPERTIES OF FACTORED POLICIES . In this section , we discuss the differences between both factorization methods from a theoretical point of view and the impact on their use in practice . We study in particular how the factorization choice affects the expression of the policy entropy and the Kullback-Leibler divergence between two factored policies . Entropies and KL divergences between policies are used in many RL algorithms as regularization terms in order to favor exploration during policy improvement or to prevent policy updates from modifying the current policy too much . When the policy is factored into independent sub-policies , we show that these quantities can easily be computed as the sum of the same quantities computed over the sub-policies . It is not as simple when the policy is autoregressive , but in this case , when computed over actions sampled from the current policy , the sum of the sub-entropies or sub-KL divergences has actually for expected value the global entropy or global KL divergence . The proofs of all propositions are in Appendix B .

Summary: The paper highlights the difficulty of training with large action space in reinforcement learning. This is usually difficult due to the vast number of possibilities during exploration. They address this issue by studying existing approaches of splitting the action into a finite number of sub-actions and then sampling each sub-action independently or auto-regressively. This leads to a reduction in the candidate actions during exploration which would improve the sample efficiency.  Also, This splitting is referred to as “Factorization of Action Space”.