Sample efficiency is crucial for imitation learning methods to be applicable in real-world applications. Many studies improve sample efficiency by extending adversarial imitation to be off-policy regardless of the fact that these off-policy extensions could either change the original objective or involve complicated optimization. We revisit the foundation of adversarial imitation and propose an off-policy sample efficient approach that requires no adversarial training or min-max optimization. Our formulation capitalizes on two key insights: (1) the similarity between the Bellman equation and the stationary state-action distribution equation allows us to derive a novel temporal difference (TD) learning approach; and (2) the use of a deterministic policy simplifies the TD learning. Combined, these insights yield a practical algorithm, Deterministic and Discriminative Imitation (D2-Imitation), which oper- ates by first partitioning samples into two replay buffers and then learning a deterministic policy via off-policy reinforcement learning. Our empirical results show that D2-Imitation is effective in achieving good sample efficiency, outperforming several off-policy extension approaches of adversarial imitation on many control tasks.

Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency

Agents that interact with other agents often do not know a priori what the other agents' strategies are, but have to maximise their own online return while interacting with and learning about others. The optimal adaptive behaviour under uncertainty over the other agents' strategies w.r.t. some prior can in principle be computed using the Interactive Bayesian Reinforcement Learning framework. Unfortunately, doing so is intractable in most settings, and existing approximation methods are restricted to small tasks. To overcome this, we propose to meta-learn (alongside the policy) approximate belief inference by combining sequential and hierarchical VAEs. We show empirically that our approach can learn a factorised belief model that separates the other agent's permanent and temporal structure, and outperforms methods that sample from the approximate posterior or do not have this hierarchical structure. A full version of this work can be found in Zintgraf et al. (2021).

Deep Interactive Bayesian Reinforcement Learning via Meta-Learning

Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user. Previous research showed that humans give copious feedback early in training but very sparsely thereafter. In this paper, we investigate the potential of agent learning from trainers' facial expressions via interpreting them as evaluative feedback. To do so, we implemented TAMER which is a popular interactive reinforcement learning method in a reinforcement-learning benchmark problem    Infinite Mario, and conducted the first large-scale study of TAMER involving 561 participants. With designed CNN-RNN model, our analysis shows that telling trainers to use facial expressions and competition can improve the accuracies for estimating positive and negative feedback using facial expressions. In addition, our results with a simulation experiment show that learning solely from predicted feedback based on facial expressions is possible and using strong/effective prediction models or a regression method, facial responses would significantly improve the performance of agents. Furthermore, our experiment supports previous studies demonstrating the importance of bi-directional feedback and competitive elements in the training interface.

Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework (JAAMAS Track)

We present a mean-variance policy iteration (MVPI) framework for risk-averse control in a discounted infinite horizon MDP optimizing the variance of a per-step reward random variable.
MVPI enjoys great flexibility in that
any policy evaluation method and risk-neutral control method can be dropped in for risk-averse control off the shelf,
in both on- and off-policy settings.
This flexibility reduces the gap between risk-neutral control and risk-averse control and is achieved by working on a novel augmented MDP directly.
We propose risk-averse TD3 as an example instantiating MVPI,
which outperforms vanilla TD3 and many previous risk-averse control methods in challenging Mujoco robot simulation tasks under a risk-aware performance metric.
This risk-averse TD3 is the first to introduce deterministic policies and off-policy learning into risk-averse reinforcement learning,
both of which are key to the performance boost we show in Mujoco domains.

Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning

Main_Track

technical paper

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AAAI 2022

Poster/Demo Session 5 

poster

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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Shimon Whiteson

4

SHORT BIO

Presentations

Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency

Deep Interactive Bayesian Reinforcement Learning via Meta-Learning

Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework (JAAMAS Track)

Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning

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