Feature construction is of vital importance in reinforcement learning, as the quality of a value function or policy is largely determined by the corresponding features. Typical deep RL approaches use a linear output layer, which means that deep RL can be interpreted as a feature construction/encoding network followed by linear value function approximation. This paper develops and evaluates a theory of linear feature encoding. We extend theoretical results on feature quality for linear value function approximation from the uncontrolled case to the controlled case. We then develop a supervised linear feature encoding method that is motivated by insights from linear value function approximation theory, as well as empirical successes from deep RL.
The goal of inverse reinforcement learning is to find a reward function for a Markov decision process, given example traces from its optimal policy. Current IRL techniques generally rely on user-supplied features that form a concise basis for the reward. We present an algorithm that instead constructs reward features from a large collection of component features, by building logical conjunctions of those component features that are relevant to the example policy. Given example traces, the algorithm returns a reward function as well as the constructed features. The reward function can be used to recover a full, deterministic, stationary policy, and the features can be used to transplant the reward function into any novel environment on which the component features are well defined.
This paper adresses the inverse reinforcement learning (IRL) problem, that is inferring a reward for which a demonstrated expert behavior is optimal. We introduce a new algorithm, SCIRL, whose principle is to use the so-called feature expectation of the expert as the parameterization of the score function of a multi-class classifier. This approach produces a reward function for which the expert policy is provably near-optimal. Contrary to most of existing IRL algorithms, SCIRL does not require solving the direct RL problem. Moreover, with an appropriate heuristic, it can succeed with only trajectories sampled according to the expert behavior. This is illustrated on a car driving simulator.
Inverse reinforcement learning (IRL) techniques (Ng and Russell, 2000) provide a foundation for detecting abnormal agent behavior and predicting agent intent through estimating its reward function. Unfortunately, IRL algorithms suffer from the large dimensionality of the reward function space. Meanwhile, most applications that can benefit from an IRL-based approach to assessing agent intent, involve interaction with an analyst or domain expert. This paper proposes a procedure for scaling up IRL by eliciting good IRL basis functions from the domain expert. Further, we propose a new paradigm for modeling limited rationality. Unlike traditional models of limited rationality that assume an agent making stochastic choices with the value function being treated as if it is known, we propose that observed irrational behavior is actually due to uncertainty about the cost of future actions. This treatment normally leads to a POMDP formulation which is unnecessarily complicated, and we show that adding a simple noise term to the value function approximation accomplishes the same at a much smaller cost.
Feature reinforcement learning was introduced five years ago as a principled and practical approach to history-based learning. This paper examines the progress since its inception. We now have both model-based and model-free cost functions, most recently extended to the function approximation setting. Our current work is geared towards playing ATARI games using imitation learning, where we use Feature RL as a feature selection method for high-dimensional domains.