Decision Making

CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. Source: Dosovitskiy et al. Image source: Dosovitskiy et al.

Soft Actor Critic, or SAC, is an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework. In this framework, the actor aims to maximize expected reward while also maximizing entropy. That is, to succeed at the task while acting as randomly as possible. Prior deep RL methods based on this framework have been formulated as Q-learning methods. SAC combines off-policy updates with a stable stochastic actor-critic formulation. The SAC objective has a number of advantages. First, the policy is incentivized to explore more widely, while giving up on clearly unpromising avenues. Second, the policy can capture multiple modes of near-optimal behavior. In problem settings where multiple actions seem equally attractive, the policy will commit equal probability mass to those actions. Lastly, the authors present evidence that it improves learning speed over state-of-art methods that optimize the conventional RL objective function.

Generative Adversarial Imitation Learning presents a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning.

BCI MI framework to classifiy brain signals using a multimodal decission making phase, with an addtional differentiation of the signal.

Neural Additive Models (NAMs) make restrictions on the structure of neural networks, which yields a family of models that are inherently interpretable while suffering little loss in prediction accuracy when applied to tabular data. Methodologically, NAMs belong to a larger model family called Generalized Additive Models (GAMs). NAMs learn a linear combination of networks that each attend to a single input feature: each $f\_{i}$ in the traditional GAM formulationis parametrized by a neural network. These networks are trained jointly using backpropagation and can learn arbitrarily complex shape functions. Interpreting NAMs is easy as the impact of a feature on the prediction does not rely on the other features and can be understood by visualizing its corresponding shape function (e.g., plotting $f\_{i}\left(x\_{i}\right)$ vs. $x\_{i}$).

Go-Explore is a family of algorithms aiming to tackle two challenges with effective exploration in reinforcement learning: algorithms forgetting how to reach previously visited states ("detachment") and from failing to first return to a state before exploring from it ("derailment"). To avoid detachment, Go-Explore builds an archive of the different states it has visited in the environment, thus ensuring that states cannot be forgotten. Starting with an archive beginning with the initial state, the archive is built iteratively. In Go-Explore we: (a) Probabilistically select a state from the archive, preferring states associated with promising cells. (b) Return to the selected state, such as by restoring simulator state or by running a goal-conditioned policy. (c) Explore from that state by taking random actions or sampling from a trained policy. (d) Map every state encountered during returning and exploring to a low-dimensional cell representation. (e) Add states that map to new cells to the archive and update other archive entries.

Soft Actor Critic (Autotuned Temperature is a modification of the SAC reinforcement learning algorithm. SAC can suffer from brittleness to the temperature hyperparameter. Unlike in conventional reinforcement learning, where the optimal policy is independent of scaling of the reward function, in maximum entropy reinforcement learning the scaling factor has to be compensated by the choice a of suitable temperature, and a sub-optimal temperature can drastically degrade performance. To resolve this issue, SAC with Autotuned Temperature has an automatic gradient-based temperature tuning method that adjusts the expected entropy over the visited states to match a target value.

CLIPort, a language-conditioned imitation-learning agent that combines the broad semantic understanding (what) of CLIP [1] with the spatial precision (where) of Transporter [2].

The Enhanced Fusion Framework proposes three different ideas to improve the existing MI-based BCI frameworks. Image source: Fumanal-Idocin et al.

Primal Wasserstein Imitation Learning, or PWIL, is a method for imitation learning which ties to the primal form of the Wasserstein distance between the expert and the agent state-action distributions. The reward function is derived offline, as opposed to recent adversarial IL algorithms that learn a reward function through interactions with the environment, and requires little fine-tuning.

This method proposes first discretizing observations and calculating the action distribution distance under comparable cases (intersection states).

ARM-Net is an adaptive relation modeling network tailored for structured data, and a lightweight framework ARMOR based on ARM-Net for relational data analytics. The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature. The authors propose a novel sparse attention mechanism to dynamically generate the interaction weights given the input tuple, so that we can explicitly model cross features of arbitrary orders with noisy features filtered selectively. Then during model inference, ARM-Net can specify the cross features being used for each prediction for higher accuracy and better interpretability.

DecomCAM decomposes intermediate activation maps into orthogonal features using singular value decomposition and generates saliency maps by integrating them.

Non-parametric approximation of Q-values by storing all visited states and doing inference through k-Nearest Neighbors.

Bayesian Reward Extrapolation is a Bayesian reward learning algorithm that scales to high-dimensional imitation learning problems by pre-training a low-dimensional feature encoding via self-supervised tasks and then leveraging preferences over demonstrations to perform fast Bayesian inference.

A closed-loop evaluation procedure is first used in a simulator to identify training data samples that are important for practical driving performance and then we these samples to help debias the policy network.

Robust Predictable Control, or RPC, is an RL algorithm for learning policies that uses only a few bits of information. RPC brings together ideas from information bottlenecks, model-based RL, and bits-back coding. The main idea of RPC is that if the agent can accurately predict the future, then the agent will not need to observe as many bits from future observations. Precisely, the agent will learn a latent dynamics model that predicts the next representation using the current representation and action. In addition to predicting the future, the agent can also decrease the number of bits by changing its behavior. States where the dynamics are hard to predict will require more bits, so the agent will prefer visiting states where its learned model can accurately predict the next state.

A method to convert a Tree Ensemble model into a Rule list. This makes the AI model more transparent.

Stefano Guarnieri, Francesco Piazza, and Aurelio Uncini "Multilayer Feedforward Networks with Adaptive Spline Activation Function," IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 3, MAY 1999 Abstract — In this paper, a new adaptive spline activation function neural network (ASNN) is presented. Due to the ASNN’s high representation capabilities, networks with a small number of interconnections can be trained to solve both pattern recognition and data processing real-time problems. The main idea is to use a Catmull–Rom cubic spline as the neuron’s activation function, which ensures a simple structure suitable for both software and hardware implementation. Experimental results demonstrate improvements in terms of generalization capability and of learning speed in both pattern recognition and data processing tasks. Index Terms— Adaptive activation functions, function shape autotuning, generalization, generalized sigmoidal functions, multilayer perceptron, neural networks, spline neural networks.