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1. Influencing Reinforcement Learning through Natural Language Guidance

   https://doi.org/10.32473/flairs.v34i1.128472  

   Tasrin, Tasmia; Nahian, Md Sultan; Perera, Habarakadage; Harrison, Brent (May 2021, The International FLAIRS Conference Proceedings)

   null (Ed.)

   Interactive reinforcement learning (IRL) agents use human feedback or instruction to help them learn in complex environments. Often, this feedback comes in the form of a discrete signal that’s either positive or negative. While informative, this information can be difficult to generalize on its own. In this work, we explore how natural language advice can be used to provide a richer feedback signal to a reinforcement learning agent by extending policy shaping, a well-known IRL technique. Usually policy shaping employs a human feedback policy to help an agent to learn more about how to achieve its goal. In our case, we replace this human feedback policy with policy generated based on natural language advice. We aim to inspect if the generated natural language reasoning provides support to a deep RL agent to decide its actions successfully in any given environment. So, we design our model with three networks: first one is the experience driven, next is the advice generator and third one is the advice driven. While the experience driven RL agent chooses its actions being influenced by the environmental reward, the advice driven neural network with generated feedback by the advice generator for any new state selects its actions to assist the RL agent to better policy shaping. 

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2. Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution

   Gupta, Piyush; Puri, Nikaash; Verma, Sukriti; Kayastha, Dhruv; Deshmukh, Shripad; Krishnamurthy, Balaji; Singh, Sameer (April 2020, International Conference on Learning Representations (ICLR))

   As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent. Our proposed approach, SARFA (Specific and Relevant Feature Attribution), generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency. The first captures the impact of perturbation on the relative expected reward of the action to be explained. The second downweighs irrelevant features that alter the relative expected rewards of actions other than the action to be explained. We compare SARFA with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders). We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches. 

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3. Multi-Agent Reinforcement Learning for Alternating-Time Logic

   Hahna, Ernst Moritz; Perez, Mateo; Schewe, Sven; Somenzi, Fabio; Trivedi, Ashutosh; Wojtczak, Dominik (October 2024, Open Access by IOS Press)

   Endriss, Ulle; Melo, Francisco (Ed.)

   Alternating-time temporal logic (ATL) extends branching time logic by enabling quantification over paths that result from the strategic choices made by multiple agents in various coalitions within the system. While classical temporal logics express properties of “closed” systems, ATL can express properties of “open” systems resulting from interactions among several agents. Reinforcement learning (RL) is a sampling-based approach to decision-making where learning agents, guided by a scalar reward function, discover optimal policies through repeated interactions with the environment. The challenge of translating high-level objectives into scalar rewards for RL has garnered increased interest, particularly following the success of model-free RL algorithms. This paper presents an approach for deploying model-free RL to verify multi-agent systems against ATL specifications. The key contribution of this paper is a verification procedure for model-free RL of quantitative and non-nested classic ATL properties, based on Q-learning, demonstrated on a natural subclass of non-nested ATL formulas. 

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4. Translating Omega-Regular Specifications to Average Objectives for Model-Free Reinforcement Learning

   https://doi.org/10.5555/3535850.3535933  

   Milad Kazemi; Mateo Perez; Fabio Somenzi; Sadegh Soudjani; Ashutosh Trivedi; Alvaro Velasquez (May 2022, Proc. of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022),)

   Piotr Faliszewski; Viviana Mascardi (Ed.)

   Recent success in reinforcement learning (RL) has brought renewed attention to the design of reward functions by which agent behavior is reinforced or deterred. Manually designing reward functions is tedious and error-prone. An alternative approach is to specify a formal, unambiguous logic requirement, which is automatically translated into a reward function to be learned from. Omega-regular languages, of which Linear Temporal Logic (LTL) is a subset, are a natural choice for specifying such requirements due to their use in verification and synthesis. However, current techniques based on omega-regular languages learn in an episodic manner whereby the environment is periodically reset to an initial state during learning. In some settings, this assumption is challenging or impossible to satisfy. Instead, in the continuing setting the agent explores the environment without resets over a single lifetime. This is a more natural setting for reasoning about omega-regular specifications defined over infinite traces of agent behavior. Optimizing the average reward instead of the usual discounted reward is more natural in this case due to the infinite-horizon objective that poses challenges to the convergence of discounted RL solutions. We restrict our attention to the omega-regular languages which correspond to absolute liveness specifications. These specifications cannot be invalidated by any finite prefix of agent behavior, in accordance with the spirit of a continuing problem. We propose a translation from absolute liveness omega-regular languages to an average reward objective for RL. Our reduction can be done on-the-fly, without full knowledge of the environment, thereby enabling the use of model-free RL algorithms. Additionally, we propose a reward structure that enables RL without episodic resetting in communicating MDPs, unlike previous approaches. We demonstrate empirically with various benchmarks that our proposed method of using average reward RL for continuing tasks defined by omega-regular specifications is more effective than competing approaches that leverage discounted RL. 

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5. InitLight: Initial Model Generation for Traffic Signal Control Using Adversarial Inverse Reinforcement Learning

   Ye, Yutong; Zhou, Yingbo; Ding, Jiepin; Wang, Ting; Chen, Mingsong; Lian, Xiang (January 2023, IJCAI)

   Due to repetitive trial-and-error style interactions between agents and a fixed traffic environment during the policy learning, existing Reinforcement Learning (RL)-based Traffic Signal Control (TSC) methods greatly suffer from long RL training time and poor adaptability of RL agents to other complex traffic environments. To address these problems, we propose a novel Adversarial Inverse Reinforcement Learning (AIRL)-based pre-training method named InitLight, which enables effective initial model generation for TSC agents. Unlike traditional RL-based TSC approaches that train a large number of agents simultaneously for a specific multi-intersection environment, InitLight pretrains only one single initial model based on multiple single-intersection environments together with their expert trajectories. Since the reward function learned by InitLight can recover ground-truth TSC rewards for different intersections at optimality, the pre-trained agent can be deployed at intersections of any traffic environments as initial models to accelerate subsequent overall global RL training. Comprehensive experimental results show that, the initial model generated by InitLight can not only significantly accelerate the convergence with much fewer episodes, but also own superior generalization ability to accommodate various kinds of complex traffic environments. 

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