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Title: Deep Reinforcement Learning with Implicit Human Feedback

Paper content:
We consider the following central question in the field of Deep Reinforcement Learning (DRL):
How can we use implicit human feedback to accelerate and optimize the training of a DRL algorithm?
State-of-the-art methods rely on any human feedback to be provided explicitly, requiring the active participation of humans (e.g., expert labeling, demonstrations, etc.).
In this work, we investigate an alternative paradigm, where non-expert humans are silently observing (and assessing) the agent interacting with the environment.
The human's intrinsic reactions to the agent's behavior is sensed as implicit feedback by placing electrodes on the human scalp and monitoring what are known as event-related electric potentials.
The implicit feedback is then used to augment the agent's learning in the RL tasks.
We develop a system to obtain and accurately decode the implicit human feedback (specifically error-related event potentials) for state-action pairs in an Atari-type environment.
As a baseline contribution, we demonstrate the feasibility of capturing error-potentials  of a human observer watching an agent learning to play several different Atari-games using an electroencephalogram (EEG) cap, and then decoding the signals appropriately and using them as an auxiliary reward function to a DRL algorithm with the intent of accelerating its learning of the game.
Building atop the baseline, we then make the following novel contributions in our work:
(i) We argue that the definition of error-potentials is generalizable across different environments; specifically we show that error-potentials of an observer can be learned for a specific game, and the definition used as-is for another game without requiring re-learning of the error-potentials.
(ii) We propose two different frameworks to combine recent advances in DRL into the error-potential based feedback system in a sample-efficient manner, allowing humans to provide implicit feedback while training in the loop, or prior to the training of the RL agent.
(iii) Finally, we scale the implicit human feedback (via ErrP) based RL to reasonably complex environments (games) and demonstrate the significance of our approach through synthetic and real user experiments.
Deep Reinforcement Learning (DRL) algorithms have now beaten human experts in Go (Silver et al., 2017) , taught robots to become parkour masters , and enabled truly autonomous vehicles (Wang et al., 2018) .
However, current state-of-the-art RL agents equipped with deep neural networks are inherently complex, difficult and time-intensive to train.
Particularly in complex environments with sparse reward functions (e.g., maze navigation), the DRL agents need an inordinate amount of interaction with the environment to learn the optimal policy.
Human participation can potentially help DRL algorithms by accelerating their training and reducing the learning costs without compromising final performance.
This potential has inspired a several research efforts where either an alternative (or supplementary) feedback is obtained from the human participant (Knox, 2012) .
Such approaches despite being highly effective, severely burden the human-in-the-loop demanding either expert demonstrations (Ross et al., 2011) or explicit feedback (Christiano et al., 2017) .
In this paper, we investigate an alternative paradigm that substantially increases the richness of the reward functions, while not severely burdening the human-in-the-loop.
We study the use of electroencephalogram (EEG) based brain waves of the human-in-the-loop to generate the reward functions that can be used by the DRL algorithms.
Such a model will benefit from the natural rich activity of a powerful sensor (the human brain), but at the same time not burden the human if the activity being relied upon is intrinsic.
This paradigm is inspired by a high-level error-processing system in humans that generates error-related potential/negativity (ErrP or ERN) (Scheffers et al., 1996) .When
a human recognizes an error made by an agent, the elicited ErrP can be captured through EEG to inform agent about the sub-optimality of the taken action in the particular state.
As a baseline contribution, we demonstrate the feasibility of capturing error-potentials of a human observer watching an agent learning to play several different Atari-games, and then decoding the signals appropriately and using them as an auxiliary reward function to a DRL algorithm.
We show that a full access approach to obtain feedback on every state-action pair while RL agent is learning, can significantly speedup the training convergence of RL agent.
We contend that while obtaining such implicit human feedback through EEG is less burdensome, it is still a time-intensive task for the subject and the experimenter alike.
This, combined with the noisy EEG signals and stochasticity in inferring error-potentials, raises significant challenges in terms of the practicality of the solution.
In this context, we first argue that the definition of ErrPs is generalizable across different environments.
We show that ErrPs of an observer can be learned for a specific game, and the definition used as-is for another game without requiring re-learning of the ErrP.
This is notably different from previous approaches (Chavarriaga & Millán, 2010; Salazar-Gomez et al., 2017) , where the labeled ErrPs are obtained in the same environment (where the RL task is performed).
For any new and unseen environment, it does not require the human to go through the training phase again, and assumes no prior knowledge about the optimal state-action pairs of the environment.
We present two different frameworks to combine recent advances in DRL into the implicit human feedback mechanism (via ErrP) in a practical, sample-efficient manner.
This reduces the cost of human supervision sufficiently allowing the DRL systems to train.
Relying on Active Learning (AL) methods, our first framework allows humans to provide implicit feedback in the loop, while an RL agent is being trained.
An uncertainty based acquisition function is modeled to select the samples state-action pairs for querying the implicit human feedback.
However, as a human is always required to be in the loop, our second framework allows humans to provide their feedback implicitly before the agent starts training.
Based on the human feedback obtained during pre-training, a quality (Q) function is learned over these imperfect demonstrations to provide the supplementary reward to the RL agent.
We present results from real ErrP experiments to evaluate the acceleration in learning, and sample efficiency, in both frameworks.
In summary, the novel contributions of our work are,
1. We demonstrate the generalizability of error-potentials over various Atari-like environments (discrete grid-based navigation games, studied in this work), enabling the estimation of implicit human feedback in new and unseen environments.
2. We propose two different frameworks to combine recent advances in DRL into ErrP based feedback system in a practical, sample-efficient manner.
The first framework allows humans to provide implicit feedback while training in the loop.
Taking advantage of recent approaches in learning from imperfect demonstrations, in the second framework, the implicit human feedback is obtained prior to the training of the RL agent.
3. We scale the implicit human feedback (via ErrP) based RL to reasonably complex environments and demonstrate the significance of our approach through synthetic and real user experiments.
Daniel et al. (2015) ; El Asri et al. (2016); Wang et al. (2016) studied RL from human rankings or ratings, however rely on explicit human feedback, and assume that the feedback is noiseless.
Demonstrations have been commonly used to improve the efficiency of RL (Kim et al., 2013; Chemali & Lazaric, 2015; Piot et al., 2014) , and a common paradigm is to initialize RL algorithms with good policy or Q function (Nair et al., 2018; Hester et al., 2018; Gao et al., 2018) .
In this work, we use rely on implicit feedback from non-expert humans (via ErrPs) which is inherently noisy.
(Chavarriaga & Millán, 2010; Iturrate et al., 2010; Salazar-Gomez et al., 2017) demonstrate the benefit of ErrPs in a very simple setting (i.e., very small state-space), and use ErrP-based feedback as the only reward.
Moreover, in all of these works, the ErrP decoder is trained on a similar game (or robotic task), essentially using the knowledge that is supposed to be unknown in the RL task.
In our work, we use labeled ErrPs examples of very simple and known environments to train the ErrP decoder, and combine with the recent advances in DRL in a sample-efficient manner for reasonably complex environments.
Consider a Markov Decision Process (MDP) problem M , as a tuple < X , A, P, P 0 , R, γ >, with state-space X , action-space A, transition kernel P , initial state distribution P 0 , accompanied with reward function R, and discounting factor 0 ≤ γ ≤ 1. Here the random variable Z(s, a) denotes the accumulated discounted future rewards starting from state s and action a.
We first demonstrate the feasibility of capturing error-potentials of a human observer watching an agent learning to play several different Atari-games, and then decoding the signals appropriately and using them as an auxiliary reward function to a DRL algorithm.
Then we argue that the definition of ErrPs is generalizable across different environment.
In the ideal approach, we validate the augmentation effect of ErrP labels on RL algorithms by the full access method.
Then, in the practical approach, we propose two augmentation frameworks for RL agent, applicable to different situations.
The first is to integrate human into the training loop of RL agent based on active learning, while the second is to learn a reward function from imperfect demonstrations labeled by ErrP.
The demonstration of the generalizability of error-potentials is limited across the environments presented in the paper.
We have considered discrete grid-based reasonably complex navigation games.
The validation of the generalization to a variety of Atari and Robotic environments is the subject of the future work.
We also plan to test our framework of integrating implicit human feedback (via ErrPs) over robotic environments, and text the generalization capability of error-potentials between virtual and physical worlds.
As future work, we plan to investigate as to how machines can be assisted in RL by using intrinsic EEG-based cooperations among humans and machines.
are bandpass filtered in [0.5, 40] Hz.
Epochs of 800ms were extracted relative to pre-stimulus 200ms baseline, and were subjected to spatial filtering.
In spatial filtering, prototype responses of each class, i.e., "correct" and "erroneous", are computed by averaging all training trials in the corresponding classes("xDAWN Spatial Filter" (Rivet et al., 2009; Barachant & Congedo, 2014; ).
"xDAWN filtering" projects the EEG signals from sensor space (i.e., electrode space) to the source space (i.e., a low-dimensional space constituted by the actual neuronal ensembles in brain firing coherently).
The covariance matrix of each epoch is computed, and concatenated with the prototype responses of the class.
Further, dimensionality reduction is achieved by selecting relevant channels through backward elimination .
The filtered signals are projected to the tangent space for feature extraction.
The obtained feature vector is first normalized (using L1 norm) and fed to a regularized regression model.
A threshold value is selected for the final decision by maximizing accuracy offline on the training set.
We present the algorithm to decode the ErrP signals in Algorithm 2.
Algorithm 2: Riemannian Geometry based ErrP classification algorithm Input : raw EEG signals EEG 1 Pre-process raw EEG signals ; 2 Spatial Filtering: xDAWN Spatial Filter (nf ilter) ; 3 Electrode Selection: ElectrodeSelect (nelec, metric='riemann') ; 4 Tangent Space Projection : TangentSpace(metric = "logeuclid") Normalize using L1 norm ; 5 Regression: ElasticNet ; 6 Select decision threshold by maximizing accuracy

assistant: text: We use implicit human feedback (via error-potentials, EEG) to accelerate and optimize the training of a DRL algorithm, in a practical manner.