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Continual Reinforcement Learning (CRL) aims to develop algorithms that adapt to non-stationary sequences of tasks. A promising recent approa… (voir plus)ch utilizes Recurrent Neural Networks (RNNs) to learn contextual Markov Decision Process (MDP) embeddings. This enables a reinforcement learning (RL) agent to discern the optimality of actions across diverse tasks. In this study, we examine two critical failure modes in the learning of these contextual MDP embeddings. Specifically, we find that RNNs are prone to catastrophic forgetting, manifesting in two distinct ways: (i) embedding collapse---where agents initially learn a contextual task structure that later collapses to a single task, and (ii) embedding drift---where learning embeddings for new MDPs interferes with embeddings the RNN outputs for previous MDPs in the sequence, leading to suboptimal performance of downstream policy networks conditioned on stale embeddings. We explore the effects of various objective functions and network architectures concerning these failure modes, revealing that one of these modes consistently emerges across different setups.
We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) through predictive and adapt… (voir plus)ive directions of (functional) policy ascent.
Leveraging the connection between policy iteration and policy gradient methods, we view policy optimization algorithms as iteratively solving a sequence of surrogate objectives, local lower bounds on the original objective. We define optimism as predictive modelling of the future behavior of a policy, and hindsight adaptation as taking immediate and anticipatory corrective actions to mitigate accumulating errors from overshooting predictions or delayed responses to change.
We use this shared lens to jointly express other well-known algorithms, including model-based policy improvement based on forward search, and optimistic meta-learning algorithms.
We show connections with Anderson acceleration, Nesterov's accelerated gradient, extra-gradient methods, and linear extrapolation in the update rule.
We analyze properties of the formulation, design an optimistic policy gradient algorithm, adaptive via meta-gradient learning, and empirically highlight several design choices pertaining to acceleration, in an illustrative task.
Estimating value functions is a core component of reinforcement learning algorithms. Temporal difference (TD) learning algorithms use bootst… (voir plus)rapping, i.e. they update the value function toward a learning target using value estimates at subsequent time-steps. Alternatively, the value function can be updated toward a learning target constructed by separately predicting successor features (SF)--a policy-dependent model--and linearly combining them with instantaneous rewards. We focus on bootstrapping targets used when estimating value functions, and propose a new backup target, the
2022-06-27
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
We address the problem of credit assignment in reinforcement learning and explore fundamental questions regarding the way in which an agent … (voir plus)can best use additional computation to propagate new information, by planning with internal models of the world to improve its predictions. Particularly, we work to understand the gains and peculiarities of planning employed as forethought via forward models or as hindsight operating with backward models. We establish the relative merits, limitations and complementary properties of both planning mechanisms in carefully constructed scenarios. Further, we investigate the best use of models in planning, primarily focusing on the selection of states in which predictions should be (re)-evaluated. Lastly, we discuss the issue of model estimation and highlight a spectrum of methods that stretch from explicit environment-dynamics predictors to more abstract planner-aware models.
2019-12-31
Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)
