Multi-Agent Adversarial Inverse Reinforcement Learning Lantao Yu, Jiaming Song, Stefano Ermon Proceedings of the 36th International Conference on Machine Learning , PMLR 97:7194-7201, 2019. Abstract: This article proposes new inverse reinforcement learning (RL) algorithms to solve our defined Adversarial Apprentice Games for nonlinear learner and expert systems. The results demonstrate the superiority of the multiagent modeling approach in predicting road user behavior, their collision avoidance mechanisms, and the Post-Encroachment-Time (PET) compared to a baseline . Unlike GAIL, AIRL recovers a reward function that is more generalizable to changes in environment dynamics. 4 Adversarial Inverse Reinforcement Learning (AIRL) In practice, using full trajectories as proposed by GAN-GCL can result in high variance estimates as compared to using single state, action pairs, and our experimental results show that this results in very poor learning. Deep reinforcement learning methods can remove the need for explicit engineering of policy or value features, but still require a manually specified reward function. Generative Adversarial Imitation Learning (GAIL) is based on the inspiration of Generative Adversarial Networks (GANs) in [ 10 ], trying to combine imitation with RL, and learn to be accurate in difficult tasks without strict reward function definitions. A particular subclass of IRL methods, named Adversarial Inverse Reinforcement Learning (AIRL), learns and encapsulates the reward function in the form of a discriminator that can discern expert. Adversarial Inverse Reinforcement Learning (AIRL) is similar to GAIL but also learns a reward function at the same time and has better training stability. Convergence of the algorithms as well as the . VAIL (Variational Adversarial Imitation Learning) SQIL (Imitation Learning via Reinforcement Learning with Sparse Rewards) AIRL (Adversarial Inverse Reinforcement Learning) Two value functions can be merged into one. Abstract Reinforcement learning agents are prone to undesired behaviors due to reward mis-specification. Inverse reinforcement learning (IRL) [ 3, 4, 5] is the problem of reconstructing the utility function of a decision maker by observing its actions, namely, how can a smart adversary estimate the utility functions and constraints of a radar by observing its radiated pulses. Abstract and Figures This article proposes new inverse reinforcement learning (RL) algorithms to solve our defined Adversarial Apprentice Games for nonlinear learner and expert systems. Inverse reinforcement learning (IRL) is the field of learning an agent's objectives, values, or rewards by observing its behavior. The applications of these methods (e.g. Title: PowerPoint Presentation Author: svlevine Created Date: 10/17/2018 9:27:46 AM . Inverse reinforce-ment learning, on the other hand, seeks to learn a reward function In this work, we propose the Model-based Adversarial Inverse Reinforcement Learning (MAIRL), an end-to-end model-based policy optimization method with self-attention. rl inverse-reinforcement-learning adversarial-learning vision-and-language visual-storytelling adversarial-reward-learning Updated on Jan 19, 2021 Python opendilab / DI-engine-docs Star 113 Code Issues Pull requests DI-engine docs (Chinese and English) Abstract: ,LfDFast Lifelong Adaptive Inverse Reinforcement Learning (FLAIR) FLAIR()() . First, the control barrier function is used to guide the training of a safety critic, which leverages the knowledge of system dynamics in the sampling process without training an additional guiding policy. Adversarial Inverse Reinforcement Learning (AIRL) is similar to GAIL but also learns a reward function at the same time and has better training stability. Adversarial Inverse Reinforcement Learning (AIRL) leverages the idea of AIL, integrates a reward function approximation along with learning the policy, and shows the utility of IRL in the transfer learning setting. (NNs): a critic NN, an actor NN, an adversary NN, and a state penalty NN. Johannes Heidecke said "We might observe the behavior of a human in some specific task and learn which states of the environment the human is trying to achieve and what the concrete goals might be." ( source) Adversarial Inverse Reinforcement Learning (AIRL) # AIRL, similar to GAIL , adversarially trains a policy against a discriminator that aims to distinguish the expert demonstrations from the learned policy. behavior cloning [6]. Generalizing MaxEnt IRL and Adversarial IRL to multi-agent systems is challenging. The trained safety critic is then . . In previous work, however, AIRL has mostly been demonstrated on robotic control in artificial environments. In this paper, we propose a safety-aware adversarial inverse reinforcement learning (S-AIRL) algorithm. However, autonomous driving is a complicated problem as it involves extensive . Inverse reinforcement learning: infer reward function from roll-outs of expert policy reward Mnih et al. In the inverse reinforcement learning (RL) problem, there are two agents. The games are solved by extracting the unknown cost function of an expert by a learner using demonstrated expert's behaviors. The games. Inverse reinforcement learning (IRL) [2, 3] addresses the importance of learning reward function and thus learn reward function approximator along with policy. By adopting the self-attention dynamics model to make the computation graph end-to-end differentiable, MAIRL has the low variance for policy optimization. Learning Robust Rewards with Adversarial Inverse Reinforcement Learning. The reward function of, and then train the final strategy of this method. Extremely unstable EAIRL (Empowerment-regularized Adversarial Inverse Reinforcement Learning) Imitation learning aims to produce trajectories that match a given expert distribution and can be attempted with techniques as simple as supervised learning - a.k.a. GAIL, AIRL) are mostly verified with control tasks in OpenAI Gym. Its extension to multi-agent settings, however, is difficult due to the more complex notions of rational behaviors. Adversarial Inverse Reinforcement Learning (AIRL) [] proposes an IRL algorithm in adversarial learning, which shows a promising result when there is considerable variability in the environment from the demonstration setting. Inverse reinforcement learning provides a framework to automatically acquire suitable reward functions from expert demonstrations. But the reward function approximator that enables transfer learning does not perform well in imitation tasks. 29 Summary Field Goal Example Advantages Problems Reinforcement Learning Optimize expected discounted A learner agent seeks to mimic another expert agent's state and control input behavior trajectories by observing the expert's behavior trajectories. Reinforcement learning provides a powerful and general framework for decision making and control, but its application in practice is often hindered by the need for extensive feature and reward engineering. Learning Robust Rewards with Adversarial Inverse Reinforcement Learning Justin Fu, Katie Luo, Sergey Levine Reinforcement learning provides a powerful and general framework for decision making and control, but its application in practice is often hindered by the need for extensive feature and reward engineering. tation learning and inverse reinforcement learning (IRL). Multi-Agent Adversarial Inverse Reinforcement Learning In this paper, we consider the IRL problem in multi-agent environments with high-dimensional continuous state-action space and unknown dynamics. Inverse reinforcement . Adversarial Inverse Reinforcement Learning To address the reward ambiguity problem, AIRL employs an additional shaping term to mitigate the effects of unwanted shaping. Adversarial Inverse Reinforcement Learning: problems Inherited GAN problems: Dicult to optimize Dicult to ne-tune Mode collapse Reward is not the last version of the network, it is curriculum set. 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