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Mungojerrie is an extensible tool that provides a framework to translate linear-time objectives into reward for reinforcement learning (RL). The tool provides convergent RL algorithms for stochastic games, reference implementations of existing reward translations for omega-regular objectives, and an internal probabilistic model checker for omega-regular objectives. This functionality is modular and operates on shared data structures, which enables fast development of new translation techniques. Mungojerrie supports finite models specified in PRISM and omega-automata specified in the HOA format, with an integrated command line interface to external linear temporal logic translators. Mungojerrie is distributed with a set of benchmarks for omega-regular objectives in RL. 

Recursion is the fundamental paradigm to finitely describe potentially infinite objects. As state-of-the-art reinforcement learning (RL) algorithms cannot directly reason about recursion, they must rely on the practitioner's ingenuity in designing a suitable "flat" representation of the environment. The resulting manual feature constructions and approximations are cumbersome and error-prone; their lack of transparency hampers scalability. To overcome these challenges, we develop RL algorithms capable of computing optimal policies in environments described as a collection of Markov decision processes (MDPs) that can recursively invoke one another. Each constituent MDP is characterized by several entry and exit points that correspond to input and output values of these invocations. These recursive MDPs (or RMDPs) are expressively equivalent to probabilistic pushdown systems (with call-stack playing the role of the pushdown stack), and can model probabilistic programs with recursive procedural calls. We introduce Recursive Q-learning---a model-free RL algorithm for RMDPs---and prove that it converges for finite, single-exit and deterministic multi-exit RMDPs under mild assumptions. 

The expanding role of reinforcement learning (RL) in safety-critical system design has promoted omega-automata as a way to express learning requirements—often non-Markovian—with greater ease of expression and interpretation than scalar reward signals. When 𝜔-automata were first proposed in model-free RL, deterministic Rabin acceptance conditions were used in an attempt to provide a direct translation from omega-automata to finite state “reward” machines defined over the same automaton structure (a memoryless reward translation). While these initial attempts to provide faithful, memoryless reward translations for Rabin acceptance conditions remained unsuccessful, translations were discovered for other acceptance conditions such as suitable, limit-deterministic Buechi acceptance or more generally, good-for-MDP Buechi acceptance conditions. Yet, the question “whether a memoryless translation of Rabin conditions to scalar rewards exists” remained unresolved. This paper presents an impossibility result implying that any attempt to use Rabin automata directly (without extra memory) for model-free RL is bound to fail. To establish this result, we show a link between a class of automata enabling memoryless reward translation to closure properties of its accepting and rejecting infinity sets, and to the insight that both the property and its complement need to allow for positional strategies for such an approach to work. We believe that such impossibility results will provide foundations for the application of RL to safety-critical systems. 

When omega-regular objectives were first proposed in model-free reinforcement learning (RL) for controlling MDPs, deterministic Rabin automata were used in an attempt to provide a direct translation from their transitions to scalar values. While these translations failed, it has turned out that it is possible to repair them by using good-for-MDPs (GFM) Buechi automata instead. These are nondeterministic Buechi automata with a restricted type of nondeterminism, albeit not as restricted as in good-for-games automata. Indeed, deterministic Rabin automata have a pretty straightforward translation to such GFM automata, which is bi-linear in the number of states and pairs. Interestingly, the same cannot be said for deterministic Streett automata: a translation to nondeterministic Rabin or Buechi automata comes at an exponential cost, even without requiring the target automaton to be good-for-MDPs. Do we have to pay more than that to obtain a good-for-MDPs automaton? The surprising answer is that we have to pay significantly less when we instead expand the good-for-MDPs property to alternating automata: like the nondeterministic GFM automata obtained from deterministic Rabin automata, the alternating good-for-MDPs automata we produce from deterministic Streett automata are bi-linear in the size of the deterministic automaton and its index. They can therefore be exponentially more succinct than the minimal nondeterministic Buechi automaton. 

Reinforcement learning is a successful explore-and-exploit approach, where a controller tries to learn how to navigate an unknown environment. The principle approach is for an intelligent agent to learn how to maximise expected rewards. But what happens if the objective refers to non-terminating systems? We can obviously not wait until an infinite amount of time has passed, assess the success, and update. But what can we do? This talk will tell. 

Omega-regular properties—specified using linear time temporal logic or various forms of omega-automata—find increasing use in specifying the objectives of reinforcement learning (RL). The key problem that arises is that of faithful and effective translation of the objective into a scalar reward for model-free RL. A recent approach exploits Büchi automata with restricted nondeterminism to reduce the search for an optimal policy for an Open image in new window-regular property to that for a simple reachability objective. A possible drawback of this translation is that reachability rewards are sparse, being reaped only at the end of each episode. Another approach reduces the search for an optimal policy to an optimization problem with two interdependent discount parameters. While this approach provides denser rewards than the reduction to reachability, it is not easily mapped to off-the-shelf RL algorithms. We propose a reward scheme that reduces the search for an optimal policy to an optimization problem with a single discount parameter that produces dense rewards and is compatible with off-the-shelf RL algorithms. Finally, we report an experimental comparison of these and other reward schemes for model-free RL with omega-regular objectives. 

This paper investigates the use of model-free reinforcement learning to compute the optimal value in two-player stochastic games with parity objectives. In this setting, two decision makers, player Min and player Max, compete on a finite game arena - a stochastic game graph with unknown but fixed probability distributions - to minimize and maximize, respectively, the probability of satisfying a parity objective. We give a reduction from stochastic parity games to a family of stochastic reachability games with a parameter ε, such that the value of a stochastic parity game equals the limit of the values of the corresponding simple stochastic games as the parameter ε tends to 0. Since this reduction does not require the knowledge of the probabilistic transition structure of the underlying game arena, model-free reinforcement learning algorithms, such as minimax Q-learning, can be used to approximate the value and mutual best-response strategies for both players in the underlying stochastic parity game. We also present a streamlined reduction from 1 1/2-player parity games to reachability games that avoids recourse to nondeterminism. Finally, we report on the experimental evaluations of both reductions 

We characterize the class of nondeterministic 𝜔-automata that can be used for the analysis of finite Markov decision processes (MDPs). We call these automata ‘good-for-MDPs’ (GFM). We show that GFM automata are closed under classic simulation as well as under more powerful simulation relations that leverage properties of optimal control strategies for MDPs. This closure enables us to exploit state-space reduction techniques, such as those based on direct and delayed simulation, that guarantee simulation equivalence. We demonstrate the promise of GFM automata by defining a new class of automata with favorable properties—they are Büchi automata with low branching degree obtained through a simple construction—and show that going beyond limit-deterministic automata may significantly benefit reinforcement learning.