Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202

Probabilistic policy reuse for safe reinforcement learning

This work introducesPolicy Reuse for Safe Reinforcement Learning, an algorithm that combines ProbabilisticPolicy Reuse and teacher advice for safe exploration in dangerous and continuous state and action reinforce-ment learning problems in which the dynamic behavior is reasonably smooth and the space is Euclidean. Thealgorithm uses a continuously increasing monotonic risk function that allows for the identification of theprobability to end up in failure from a given state. Such a risk function is defined in terms of how far such astate is from the state space known by the learning agent. Probabilistic Policy Reuse is used to safely balancethe exploitation of actual learned knowledge, the exploration of new actions, and the request of teacher advicein parts of the state space considered dangerous. Specifically, thepi-reuse exploration strategy is used. Usingexperiments in the helicopter hover task and a business management problem, we show that thepi-reuseexploration strategy can be used to completely avoid the visit to undesirable situations while maintainingthe performance (in terms of the classical long-term accumulated reward) of the final policy achieved.This paper has been partially supported by the Spanish Ministerio de Economía y Competitividad TIN2015-65686-C5-1-R and the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement No. 730086 (ERGO). Javier García is partially supported by the Comunidad de Madrid (Spain) funds under the project 2016-T2/TIC-1712

Regret Bounds for Reinforcement Learning with Policy Advice

In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with policy advice (RLPA) algorithm which leverages this input set and learns to use the best policy in the set for the reinforcement learning task at hand. We prove that RLPA has a sub-linear regret of \tilde O(\sqrt{T}) relative to the best input policy, and that both this regret and its computational complexity are independent of the size of the state and action space. Our empirical simulations support our theoretical analysis. This suggests RLPA may offer significant advantages in large domains where some prior good policies are provided