Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning

1 INTRODUCTION . The key to a wider and successful application of DRL techniques in real scenarios is the ability to adapt to the surrounding environment by generalizing from training experiences . These solutions have to cope with the uncertainties of the operational environment , requiring a huge number of trials to achieve good performance . Hence , devising robust learning approaches while improving sample efficiency is one of the challenges for wider utilization of DRL . Despite the promising results ( Tai et al. , 2017 ; Zhang et al. , 2017 ; Marchesini et al. , 2019 ) , DRL also suffer from convergence to local optima , which is mainly caused by the lack of diverse exploration when operating in high-dimensional spaces . Several studies address the exploration problem ( e.g. , curiosity-driven exploration ( Pathak et al. , 2017 ) , count-based exploration ( Ostrovski et al. , 2017 ) ) , but they typically rely on sensitive task-specific hyper-parameters . The sensitivity to such hyper-parameters is another significant issue in DRL as it typically results in brittle convergence properties and poor performance in practical tasks ( Haarnoja et al. , 2018 ) . Evolutionary Algorithms ( Fogel , 2006 ) have been recently employed as a promising gradient-free optimization alternative to DRL . The redundancy of these population-based approaches has the advantages of enabling diverse exploration and improve robustness , leading to a more stable convergence . In particular , Genetic Algorithms ( GA ) ( Montana & Davis , 1989 ) show competitive results compared to gradient-based DRL ( Such et al. , 2017 ) and are characterized by low computational cost . These gradient-free approaches , however , struggle to solve high-dimensional problems having poor generalization skills and are significantly less sample efficient than gradient-based methods . An emergent research direction proposes the combination of gradient-free and gradient-based methods following the physical world , where evolution and learning cooperate to assimilate the best of both solutions ( Simpson , 1953 ) . The first mixed approach , Evolutionary Reinforcement Learning ( ERL ) ( Khadka & Tumer , 2018 ) , relies on actor-critic architecture to inject information in an evolutionary population while both the gradient-free and gradient-based training phases proceed in parallel . Similarly , Proximal Distilled ERL ( PDERL ) ( Bodnar , 2020 ) extends ERL with different evolutionary methods . CEM-RL ( Pourchot , 2019 ) brings this research direction into the family of distributed approaches , combining a portfolio of TD3 ( Fujimoto et al. , 2018 ) learners with the Cross-Entropy Method ( Yan Duan , 2016 ) . ∗Contact author : enrico.marchesini @ univr.it These mixed approaches however also present several limitations , which we address through our work : ( i ) the parallel training phases of the DRL and EA components ( Khadka & Tumer , 2018 ; Bodnar , 2020 ) , or the multitude of learners ( Pourchot , 2019 ) result in significant overhead ( detailed in Section 4 ) . ( ii ) The actor-critic formalization of previous mixed approaches , allows them to be easily evaluated in continuous locomotion benchmarks ( Brockman et al. , 2016 ; Todorov et al. , 2012 ) . However , this also hinders their combination with value-based DRL ( Marchesini & Farinelli , 2020a ) . This is important as recent work ( Matheron et al. , 2019 ) shows the limitation of actor-critic in deterministic tasks , that in contrast can be effectively addressed with value-based DRL . In particular , Section 4 shows that a value-based implementation of Khadka & Tumer ( 2018 ) does not converge in our discrete robotic task . ( iii ) The combination strategy does not ensure better performance compared to the DRL agent as it does not prevent detrimental behaviours ( e.g. , drop in performance ) . This is shown in the poor performance of a value-based implementation of ERL and PDERL ( Section 4 ) . We propose a novel mixed framework , called Soft Updates for Policy Evolution ( Supe-RL ) , that enables us to combine the characteristics of GAs with any DRL algorithm , addressing the limitations of previous approaches . Supe-RL ( Figure 1 ) benefits from the high sampling efficiency of gradientbased DRL while incorporating gradient-free GA to generate diverse experiences and find better policies . Summarizing , Supe-RL based algorithms perform a periodical genetic evaluation applying GAs to the agent network . A selection operator uses a fitness metric to evaluate the population , choosing the best performing genome ( i.e. , the weights of the network ) that is used to update the weights of the DRL agent . In contrast to previous work , our genetic evaluation is only performed periodically , drastically reducing the overhead . Furthermore , our soft update ( Section 3 ) allows a direct integration of GAs to any DRL algorithm as it is similar to perform a gradient step towards a better policy , avoiding detrimental behaviours . As detailed in Section 3.1 , this allows using valuebased DRL , exploiting the variety of optimizations developed for the well-known DQN ( van Hasselt et al. , 2016 ; Schaul et al. , 2016 ; Wang et al. , 2016 ; Fortunato et al. , 2017 ; Bellemare et al. , 2017 ) . Crucially , our genetic component influences the DRL agent policy only if one of its mutated version performs better in a subset of evaluation episodes . Hence , as detailed in Section 3 , with a sufficient number of episodes we obtain a good estimation of the overall performance of the population . Our evaluation focuses on mapless navigation , a well-known problem in robotics and recent DRL ( Zhang et al. , 2017 ; Wahid et al. , 2019 ; Marchesini & Farinelli , 2020b ) . In particular , we consider two tasks developed with Unity ( Juliani et al. , 2018 ) : ( i ) a discrete action space indoor scenario with obstacles for a mobile robot and ( ii ) a continuous task for aquatic drones , with dynamic waves and physically realistic water . Besides considering standard metrics related to performance ( success rate and reward ) , we also consider safety properties that are particularly important in these domains ( e.g. , the agent does not collide with obstacles ) . In more detail , we employ formal verification ( ( Corsi et al. , 2020 ) ) to compute the percentage of input cases that cause violations of these properties . This is important to confirm our claim that Supe-RL based approaches correctly bias the exploration process in the direction of more robust policy regions with higher returns . Results show that Supe-RL algorithms improve performance ( i.e. , training time , success rate , average reward ) , stability , and safety over value-based and policy-gradient DRL ( Rainbow ( Hessel et al. , 2018 ) , PPO ( Schulman et al. , 2017 ) ) and ERL . Finally , we performed additional comparisons of Supe-RL with : ( i ) PDERL ( Bodnar , 2020 ) to evidence the poor performance of previous mixed approaches when combined with value-based DRL ; ( ii ) CEM-RL ( Pourchot , 2019 ) in the aquatic scenario , to show the differences with a multi learner approach ; ( iii ) ERL in standard continuous benchmarks ( i.e. , MuJoCo locomotion ( Brockman et al. , 2016 ; Todorov et al. , 2012 ) ) , where results confirm the superior performance of Supe-RL . 2 BACKGROUND AND RELATED WORK . We formalize robotic navigation as a RL problem , defined over a Markov Decision Process , as described in recent DRL literature ( Tai et al. , 2017 ; Zhang et al. , 2017 ; Wahid et al. , 2019 ) . DRL for robotic navigation focus exclusively on continuous action algorithms such as actor-critic DDPG ( Lillicrap et al. , 2015 ) , TD3 ( Fujimoto et al. , 2018 ) and PPO ( Schulman et al. , 2017 ) . Such methods have been adopted following the idea that value-based DQN ( Mnih et al. , 2013 ) can not deal with high-dimensional action spaces . However , discrete value-based solutions typically result in shorter training time being more sample efficient , and show better performance even in continuous settings . In detail , Marchesini & Farinelli ( 2020b ) shows that discrete DRL is a more efficient alternative to continuous DRL in robotic navigation . Moreover , Tavakoli et al . ( 2018 ) proposes an adaptation of Dueling DQN ( Wang et al. , 2016 ) with Double DQN ( van Hasselt et al. , 2016 ) that achieves competitive results in locomotion benchmarks ( Brockman et al. , 2016 ; Todorov et al. , 2012 ) . More recently , de Wiele et al . ( 2020 ) designed a DQN-based algorithm to handle enormous discrete and continuous action spaces . These researches further motivate our contribution in the design of a mixed approach that also works with value-based DRL . Evolutionary Algorithms EA are an alternative black-box optimization characterized by three main operators ( Fogel , 2006 ) : generation , alteration , and selection . In detail , Montana & Davis ( 1989 ) evolves a population of N individuals , each one represented by the network vector parameter θ ( genome ) . Each θi ( i ∈ [ 0 , .. , N − 1 ] ) is evaluated to produce a fitness F ( θi ) , used by the selection operator to choose the best genome . For the EA component of Supe-RL , we consider a mutationbased GA that shown competitive performance over gradient-based DRL ( Such et al. , 2017 ) . Combining EA and DRL Following the trend of using EA as an alternative for DRL ( Salimans et al. , 2017 ) , an emergent research field focuses on combining gradient-free and gradient-based solutions . In particular , ERL ( Khadka & Tumer , 2018 ) considers an actor-critic DDPG agent ( Lillicrap et al. , 2015 ) and a concurrent EA training that generates a population of individuals , which are mutated and selected based on their fitness . The DRL agent is trained in parallel from the samples generated by both the training phases and it is periodically injected in the running population which is used to collect the training performance . The mutation function of ERL ensures that , in a certain number of episodes , the gradient-based policy outperforms its evolutionary siblings , introducing the gradient-based benefits into the population . Hence , biasing the selection process of the next generation and its performance . In their experiments , the authors highlight an efficient transfer of information between the two families of algorithms , outperforming DDPG in well-known locomotion benchmarks ( Brockman et al. , 2016 ; Todorov et al. , 2012 ) . However , both the introduction of all the experiences in the buffer and forcing the DRL agent to perform better than the EA population , bias the training and can cause detrimental behaviours . Inspired by ERL , several combinations have been proposed ( Bodnar , 2020 ; Colas et al. , 2018 ; Pourchot , 2019 ; Khadka et al. , 2019 ) . While GEP-PG ( Bodnar , 2020 ) can be considered as a simplified version of a mixed approach , where a curiositydriven approach is used to fill the buffer of the agent , Proximal Distilled ERL ( PDERL ) ( Bodnar , 2020 ) addresses the EA component of ERL , introducing novel operators to compensate for the simplicity of the genetic representation ( as investigated by Lehman et al . ( 2018 ) , where authors address destructive behaviors of biologically-inspired variation operators applied to neural networks , which causes catastrophic forgetting ) . However , as detailed in Section 3.1 , our genetic evaluation is used to soft update the DRL agent only in the case of performance improvement , hence it does not show such catastrophic forgetting . We also mention CERL ( Khadka et al. , 2019 ) and CEM-RL ( Pourchot , 2019 ) as they are extensions of ERL for distributed training ( which we do not consider here ) with multiple active learners , which leads to non-negligible overhead ( nonetheless , Section 4.2 reports an additional experiment in our continuous task with CEM-RL , to provide a more heterogeneous overview of the superior performance of Supe-RL ) . These works share a common baseline as they all rely on actor-critic DRL and are built on the insights of ERL , which is the most closely related to Supe-RL . Hence , we choose ERL for complete performance comparison . Section 4.2 also shows a comparison with PDERL , to further highlights the poor performance of previous approaches when combined with value-based DRL . Finally , we use formal verification to support our claims on the beneficial effects of our genetic component into the policy . We report in Appendix A a brief description of the considered methodology .

Summary: The paper introduces Supe-RL that intermingles off-policy reinforcement learning with periodic beam search operations. The method makes a greedy selection between the rl-solution that it had and the best produced by the beam search using Polyak update to update the incumbent rl-solution if the one suggested by beam search is better. Experiments in robotics simulation benchmarks show that Supe-RL improves over prior hybrid methods.