Constrained episodic reinforcement learning in concave-convex and knapsack settings

NIPS 2020, 2020.

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In this paper we study constrained episodic reinforcement learning, which encompasses all of these applications

Abstract:

We propose an algorithm for tabular episodic reinforcement learning with constraints. We provide a modular analysis with strong theoretical guarantees for settings with concave rewards and convex constraints, and for settings with hard constraints (knapsacks). Most of the previous work in constrained reinforcement learning is limited to...More
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Introduction
  • Standard reinforcement learning (RL) approaches seek to maximize a scalar reward (Sutton and Barto, 1998, 2018; Schulman et al, 2015; Mnih et al, 2015), but in many settings this is insufficient, because the desired properties of the agent behavior are better described using constraints.
  • All of these approaches focus on linear reward objective and linear constraints and do not handle the concave-convex and knapsack settings that the authors consider.
Highlights
  • Standard reinforcement learning (RL) approaches seek to maximize a scalar reward (Sutton and Barto, 1998, 2018; Schulman et al, 2015; Mnih et al, 2015), but in many settings this is insufficient, because the desired properties of the agent behavior are better described using constraints
  • In this paper we study constrained episodic reinforcement learning, which encompasses all of these applications
  • Our learning algorithms optimize their actions with respect to a model based on the empirical statistics, while optimistically overestimating rewards and underestimating the resource consumption. This idea was previously introduced in multiarmed bandits (Agrawal and Devanur, 2014); extending it to episodic reinforcement learning poses additional challenges since the policy space is exponential in the episode horizon. Circumventing these challenges, we provide a modular way to analyze this approach in the basic setting where both rewards and constraints are linear (Section 3) and transfer this result to the more complicated concave-convex and knapsack settings (Sections 4 and 5)
  • We introduce a simple algorithm that allows to simultaneously effectively bound reward and consumption regrets for the basic setting introduced in the previous section. Even in this basic setting, we provide the first sample-efficient guarantees in constrained episodic reinforcement learning
  • Our experiments demonstrate that the proposed algorithm significantly outperforms these approaches in existing constrained episodic environments
  • Analogous to (1), the learner wishes to compete against the following benchmark which which can be viewed as a reinforcement learning variant of the benchmark used by Agrawal and Devanur (2014) in multi-armed bandits: max f Eπ,p π
Results
  • In the basic setting (Section 3), the learner wishes to maximize reward while respecting the consumption constraints in expectation by competing favorably against the following benchmark: H
  • The authors' main results hold more generally for concave reward objective and convex consumption constraints (Section 4) and extend to the knapsack setting where constraints are hard (Section 5).
  • Even in this basic setting, the authors provide the first sample-efficient guarantees in constrained episodic reinforcement learning.
  • The authors extend the algorithm and guarantees derived for the basic setting to when the objective is concave function of the accumulated reward and the constraints a convex function of the cumulative consumptions.
  • There is a concave reward-objective function f : R → R and a convex consumption-objective function g : Rd → R; the only assumption is that these functions are L-Lipschitz for some constant L, i.e., |f (x) − f (y)| ≤ L|x − y| for any x, y ∈ R, and |g(x) − g(y)| ≤ L x − y 1 for any x, y ∈ Rd. Analogous to (1), the learner wishes to compete against the following benchmark which which can be viewed as a reinforcement learning variant of the benchmark used by Agrawal and Devanur (2014) in multi-armed bandits: max f Eπ,p π
  • To extend the guarantee of the basic setting to the concave-convex setting, the authors face an additional challenge: it is not immediately clear that the optimal policy π is feasible for the ConvexConPlanner program , since ConvexConPlanner is defined with respect to the empirical transition probabilities p(k).6 The authors use a novel application of mean-value theorem to show that π is a feasible solution of that program.
  • With probability 1 − δ, the algorithm in the concave-convex setting has reward and consumption regret upper bounded by L · RewReg and Ld · ConsReg respectively.
Conclusion
  • As in most works on bandits with knapsacks, the algorithm is allowed to use a “null action” for an episode, i.e., an action that gives 0 reward and consumption when selected in the beginning of the episode.
  • 7 Let AggReg(δ) be a bound on the aggregate reward or consumption regret for the soft-constraint setting (Theorem 3.4) where δ is its failure probability.
Summary
  • Standard reinforcement learning (RL) approaches seek to maximize a scalar reward (Sutton and Barto, 1998, 2018; Schulman et al, 2015; Mnih et al, 2015), but in many settings this is insufficient, because the desired properties of the agent behavior are better described using constraints.
  • All of these approaches focus on linear reward objective and linear constraints and do not handle the concave-convex and knapsack settings that the authors consider.
  • In the basic setting (Section 3), the learner wishes to maximize reward while respecting the consumption constraints in expectation by competing favorably against the following benchmark: H
  • The authors' main results hold more generally for concave reward objective and convex consumption constraints (Section 4) and extend to the knapsack setting where constraints are hard (Section 5).
  • Even in this basic setting, the authors provide the first sample-efficient guarantees in constrained episodic reinforcement learning.
  • The authors extend the algorithm and guarantees derived for the basic setting to when the objective is concave function of the accumulated reward and the constraints a convex function of the cumulative consumptions.
  • There is a concave reward-objective function f : R → R and a convex consumption-objective function g : Rd → R; the only assumption is that these functions are L-Lipschitz for some constant L, i.e., |f (x) − f (y)| ≤ L|x − y| for any x, y ∈ R, and |g(x) − g(y)| ≤ L x − y 1 for any x, y ∈ Rd. Analogous to (1), the learner wishes to compete against the following benchmark which which can be viewed as a reinforcement learning variant of the benchmark used by Agrawal and Devanur (2014) in multi-armed bandits: max f Eπ,p π
  • To extend the guarantee of the basic setting to the concave-convex setting, the authors face an additional challenge: it is not immediately clear that the optimal policy π is feasible for the ConvexConPlanner program , since ConvexConPlanner is defined with respect to the empirical transition probabilities p(k).6 The authors use a novel application of mean-value theorem to show that π is a feasible solution of that program.
  • With probability 1 − δ, the algorithm in the concave-convex setting has reward and consumption regret upper bounded by L · RewReg and Ld · ConsReg respectively.
  • As in most works on bandits with knapsacks, the algorithm is allowed to use a “null action” for an episode, i.e., an action that gives 0 reward and consumption when selected in the beginning of the episode.
  • 7 Let AggReg(δ) be a bound on the aggregate reward or consumption regret for the soft-constraint setting (Theorem 3.4) where δ is its failure probability.
Tables
  • Table1: Considered Hyperparameters
  • Table2: Selected Hyperparameters
Download tables as Excel
Related work
  • Sample-efficient exploration in constrained episodic reinforcement learning has only recently started to receive attention. Most previous works on episodic reinforcement learning focus on unconstrained settings (Jaksch et al, 2010; Azar et al, 2017; Dann et al, 2017). A notable exception is the work of Cheung (2019), which provides theoretical guarantees for the reinforcement learning setting with a single episode, but requires a strong reachability assumption, which is not needed in the episodic setting studied here. Also, our results for the knapsack setting allow for a significantly smaller budget as we illustrate in Section 5. Moreover, our approach is based on a tighter bonus, which leads to a superior empirical performance (see Section 6). Recently, there have also been several concurrent and independent works on sample-efficient exploration for reinforcement learning with constraints (Singh et al, 2020; Efroni et al, 2020; Qiu et al, 2020; Ding et al, 2020). Unlike our work, all of these approaches focus on linear reward objective and linear constraints and do not handle the concave-convex and knapsack settings that we consider.
Funding
  • Work was supported by National Science Foundation under Grant
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