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This paper introduces the Relative Inequality Index at the Maximum (IDRM), a novel and intuitive measure designed to capture inequality within a population, such as income inequality. The index is based on the idea that individuals experience varying levels of inequality depending on their position within the distribution, particularly with respect to those at the top. The key assumption is that for individuals in lower positions, inequalities referenced to the top positions have greater impact on their well-being and the inequality relative to maximum is the most critical. The IDRM fulfills desirable theoretical properties which were used for its evaluation and comparison against widely accepted measures in inequality literature. From this perspective, the IDRM is shown to be as robust as traditional measures and outperforms the Gini and Dalton indices by satisfying eight out of nine key properties, including decomposability across population subgroups. In a comparative analysis using income data from 58 countries and microdata from Mexico, with the Gini, Theil, and Atkinson indices as benchmarks, the IDRM demonstrates superior consistency, sensitivity to inequality, reduced bias in grouped data, and enhanced precision. This index reflects the varying forms of income distribution, showing heightened sensitivity to the magnitude of inequality., Comment: 38 pages, in Spanish language, 6 tables, 4 figures

We present a multi-wavelength analysis of MCG+11-11-032, a nearby AGN with the unique classification of both a binary and a dual AGN candidate. With new Chandra observations we aim to resolve any dual AGN system via imaging data, and search for signs of a binary AGN via analysis of the X-ray spectrum. Analyzing the Chandra spectrum, we find no evidence of previously suggested double-peaked Fe K$\alpha$ lines; the spectrum is instead best fit by an absorbed powerlaw with a single Fe K$\alpha$ line, as well as an additional line centered at $\approx$7.5 keV. The Chandra observation reveals faint, soft, and extended X-ray emission, possibly linked to low-level nuclear outflows. Further analysis shows evidence for a compact, hard source -- MCG+11-11-032 X2 -- located 3.27'' from the primary AGN. Modeling MCG+11-11-032 X2 as a compact source, we find that it is relatively luminous ($L_{\text{2$-$10 keV}} = 1.52_{-0.48}^{+0.96}\times 10^{41}$ erg s$^{-1}$), and the location is coincident with an compact and off-nuclear source resolved in Hubble Space Telescope infrared (F105W) and ultraviolet (F621M, F547M) bands. Pairing our X-ray results with a 144 MHz radio detection at the host galaxy location, we observe X-ray and radio properties similar to those of ESO 243-49 HLX-1, suggesting that MCG+11-11-032 X2 may be a hyper-luminous X-ray source. This detection with Chandra highlights the importance of a high-resolution X-ray imager, and how previous binary AGN candidates detected with large-aperture instruments benefit from high-resolution follow-up. Future spatially resolved optical spectra, and deeper X-ray observations, can better constrain the origin of MCG+11-11-032 X2., Comment: 16 pages, 7 figures, submitted to ApJ

Broad H$\alpha$ and H$\beta$ emission lines (FWHM > 1,000 km s$^{-1}$) are incredibly efficient tracers of the high-velocity clouds encircling Active Galactic Nuclei (AGN). As a result, we search for these broad line AGN in the Sloan Digital Sky Survey's Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) catalog. We identify 301 broad-line H$\alpha$ galaxies and 801 broad-line H$\beta$ galaxies in the catalog. In total, we detect 1,042 unique broad-line galaxies with luminosities between 10$^{37}$ - 10$^{43}$ erg s$^{-1}$; 60 feature both broad H$\alpha$ and broad H$\beta$ emission. We also determine that the broad line region radius ranges between 0.01 - 46 light days, with a median radius of 0.1 light days (0.02 pc) for our broad H$\beta$ sample. In addition, we find that both samples feature a higher fraction of galaxy mergers (44% for the broad H$\alpha$ sample and 43% for the broad H$\beta$ sample), compared to the full MaNGA galaxy sample (26%), which suggests that merger-driven fueling is strongly active in our sample., Comment: 15 pages, 7 figures

To facilitate new studies of galaxy merger driven fueling of active galactic nuclei (AGNs), we present a catalog of 387 AGNs that we have identified in the final population of over $10,000$ $z<0.15$ galaxies observed by the SDSS-IV integral field spectroscopy survey Mapping Nearby Galaxies at Apache Point Observatory (MaNGA). We selected the AGNs via mid-infrared WISE colors, Swift/BAT ultra hard X-ray detections, NVSS and FIRST radio observations, and broad emission lines in SDSS spectra. By combining the MaNGA AGN catalog with a new SDSS catalog of galaxy mergers that were identified based on a suite of hydrodynamical simulations of merging galaxies, we study the link between galaxy mergers and nuclear activity for AGNs above a limiting bolometric luminosity of $10^{44.4}$ erg s$^{-1}$. We find an excess of AGNs in mergers, relative to non-mergers, for galaxies with stellar mass $\sim10^{11}$ $M_{\odot}$, where the AGN excess is somewhat stronger in major mergers than in minor mergers. Further, when we combine minor and major mergers and sort by merger stage, we find that the highest AGN excess occurs in post-coalescence mergers in the highest mass galaxies. However, we find no evidence of a correlation between galaxy mergers and AGN luminosity or accretion rate. In summary, while galaxy mergers overall do appear to trigger or enhance AGN activity more than non-mergers, they do not seem to induce higher levels of accretion or higher luminosities. We provide the MaNGA AGN Catalog and the MaNGA Galaxy Merger Catalog for the community here., Comment: The MaNGA AGN Catalog (https://content.cld.iop.org/journals/0004-637X/963/1/53/revision1/apjad1a15t1_mrt.txt) and MaNGA Galaxy Merger Catalog (https://content.cld.iop.org/journals/0004-637X/963/1/53/revision1/apjad1a15t2_mrt.txt) are available for download from the ApJ article. The MaNGA AGN Catalog is also available as an SDSS value added catalog (https://data.sdss.org/sas/dr17/env/MANGA_AGN/)

We present James Webb Space Telescope (JWST) Integral Field Spectrograph observations of NGC 3256, a local infrared-luminous late-stage merging system with two nuclei about 1 kpc apart, both of which have evidence of cold molecular outflows. Using JWST NIRSpec and MIRI datasets, we investigate this morphologically complex system on spatial scales of $<$100 pc, where we focus on the warm molecular H$_2$ gas surrounding the nuclei. We detect collimated outflowing warm H$_2$ gas originating from the southern nucleus, though we do not find significant outflowing warm H$_2$ gas surrounding the northern nucleus. Within the observed region, the maximum intrinsic velocities of the outflow reach up to $\sim$1,000 km s$^{-1}$, and extend out to a distance of 0.7 kpc. Based on H$_2$ S(7)/S(1) line ratios, we find a larger fraction of warmer gas near the S nucleus, which decreases with increasing distance from the nucleus, signifying the S nucleus as a primary source of H$_2$ heating. The gas mass of the warm H$_2$ outflow component is estimated to be $M\rm{_{warm,out}}$ = 8.9$\times$10$^5\;M_{\odot}$, as much as 4$\%$ of the cold H$_2$ mass as estimated using ALMA CO data. The outflow time scale is about $7\times10^5$ yr, resulting in a mass outflow rate of $\dot{M}\rm{_{warm,out}}$ = 1.3 M$_{\odot}$ yr$^{-1}$ and kinetic power of $P\rm{_{warm,out}}\;\sim\;2\times10^{41}$ erg s$^{-1}$. Lastly, the regions where the outflowing gas reside show high [FeII]/Pa$\beta$ and H$_2$/Br$\gamma$ line ratios, indicating enhanced mechanical heating caused by the outflows. At the same time, the 3.3 $\mu$m and 6.2 $\mu$m Polycyclic Aromatic Hydrocarbon fluxes in these regions are not significantly suppressed compared to those outside the outflows, suggesting the outflows have no clear negative feedback effect on the local star formation., Comment: 16 pages, 8 figures. Submitted to ApJ

Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment. As the training dataset is fixed, its quality becomes a crucial determining factor in the performance of the learned policy. This paper studies a dataset characteristic that we refer to as multi-behavior, indicating that the dataset is collected using multiple policies that exhibit distinct behaviors. In contrast, a uni-behavior dataset would be collected solely using one policy. We observed that policies learned from a uni-behavior dataset typically outperform those learned from multi-behavior datasets, despite the uni-behavior dataset having fewer examples and less diversity. Therefore, we propose a behavior-aware deep clustering approach that partitions multi-behavior datasets into several uni-behavior subsets, thereby benefiting downstream policy learning. Our approach is flexible and effective; it can adaptively estimate the number of clusters while demonstrating high clustering accuracy, achieving an average Adjusted Rand Index of 0.987 across various continuous control task datasets. Finally, we present improved policy learning examples using dataset clustering and discuss several potential scenarios where our approach might benefit the offline policy learning community.

This work proposes a geographically-based split of the community microgrids into clusters of members that tend to have similar consumption and generation profiles. Assuming a community microgrid divided into clusters, a two-layer architecture is developed to facilitate the greater penetration of distributed energy resources in an efficient way. The first layer, referred as the market layer, is responsible for creating local energy markets with the aim of maximising the economic benefits for community microgrid members. The second layer is responsible for the network reconfiguration, which is based on the energy balance within each cluster. This layer complies with the IEC 61850 communication standard, in order to control commercial sectionalizing and tie switches. This allows the community microgrid network to be reconfigured to minimise energy exchanges with the main grid. To implement this two-layer energy management strategy, an aperiodic market approach based on Blockchain technology, and the additional functionality offered by Smart Contracts is adopted. This embraces the concept of energy communities since it decentralizes the control and eliminates intermediaries. The use of aperiodic control techniques helps to overcome the challenges of using Blockchain technology in terms of storage, computational requirements and member privacy. The scalability and modularity of the Smart Contract-based system allow each cluster of members to be designed by tailoring the system to their specific needs. The implementation of this strategy is based on low-cost off-the-shelf devices, such as Raspberry Pi 4 Model B boards, which operate as Blockchain nodes of community microgrid members. Finally, the strategy has been validated by emulating two use cases based on the IEEE 123-node system network model highlighting the benefits of the proposal.

Hindsight Experience Replay (HER) is a technique used in reinforcement learning (RL) that has proven to be very efficient for training off-policy RL-based agents to solve goal-based robotic manipulation tasks using sparse rewards. Even though HER improves the sample efficiency of RL-based agents by learning from mistakes made in past experiences, it does not provide any guidance while exploring the environment. This leads to very large training times due to the volume of experience required to train an agent using this replay strategy. In this paper, we propose a method that uses primitive behaviours that have been previously learned to solve simple tasks in order to guide the agent toward more rewarding actions during exploration while learning other more complex tasks. This guidance, however, is not executed by a manually designed curriculum, but rather using a critic network to decide at each timestep whether or not to use the actions proposed by the previously-learned primitive policies. We evaluate our method by comparing its performance against HER and other more efficient variations of this algorithm in several block manipulation tasks. We demonstrate the agents can learn a successful policy faster when using our proposed method, both in terms of sample efficiency and computation time. Code is available at https://github.com/franroldans/qmp-her., Comment: 6 pages, 2 figures, 1 algorithm, 1 table. Version accepted to ICARA 2024

We present new JWST-NIRSpec IFS data for the luminous infrared galaxy NGC7469: a nearby (70.6Mpc) active galaxy with a Sy 1.5 nucleus that drives a highly ionized gas outflow and a prominent nuclear star-forming ring. Using the superb sensitivity and high spatial resolution of the JWST instrument NIRSpec-IFS, we investigate the role of the Seyfert nucleus in the excitation and dynamics of the circumnuclear gas. Our analysis focuses on the [Fe ii], H2, and hydrogen recombination lines that trace the radiation/shocked-excited molecular and ionized ISM around the AGN. We investigate the gas excitation through H2/Br{\gamma} and [Fe ii]/Pa\b{eta} emission line ratios and find that photoionization by the AGN dominates within the central 300 pc of the galaxy and together with a small region show ing signatures of shock-heated gas; these shock-heated regions are likely associated with a compact radio jet. In addition, the velocity field and velocity dispersion maps reveal complex gas kinematics. Rotation is the dominant feature, but we also identify non-circular motions consistent with gas inflows as traced by the velocity residuals and the spiral pattern in the Pa{\alpha} velocity dispersion map. The inflow is consistent with the mass outflow rate and two orders of magnitude higher than the AGN accretion rate. The compact nuclear radio jet has enough power to drive the highly ionized outflow. This scenario suggests that the inflow and outflow are in a self-regulating feeding-feedback process, with a contribution from the radio jet helping to drive the outflow., Comment: 15 pages, 4 figures, 3 tables, accepted for publication in ApJ

We present James Webb Space Telescope (JWST) Near Infrared Spectrograph (NIRSpec) integral-field spectroscopy of the nearby luminous infrared galaxy, NGC 7469. We take advantage of the high spatial/spectral resolution and wavelength coverage of JWST /NIRSpec to study the 3.3 um neutral polycyclic aromatic hydrocarbon (PAH) grain emission on ~60 pc scales. We find a clear change in the average grain properties between the star-forming ring and the central AGN. Regions in the vicinity of the AGN, with [NeIII]/[NeII]>0.25, tend to have larger grain sizes and lower aliphatic-to-aromatic (3.4/3.3) ratios indicating that smaller grains are preferentially removed by photo-destruction in the vicinity of the AGN. We find an overall suppression of the total PAH emission relative to the ionized gas in the central 1 kpc region of the AGN in NGC 7469 compared to what has been observed with Spitzer on 3 kpc scales. However, the fractional 3.3 um to total PAH power is enhanced in the starburst ring, possibly due to a variety of physical effects on sub-kpc scales, including recurrent fluorescence of small grains or multiple photon absorption by large grains. Finally, the IFU data show that while the 3.3 um PAH-derived star formation rate (SFR) in the ring is 8% higher than that inferred from the [NeII] and [NeIII] emission lines, the integrated SFR derived from the 3.3 um feature would be underestimated by a factor of two due to the deficit of PAHs around the AGN, as might occur if a composite system like NGC 7469 were to be observed at high-redshift., Comment: 14 pages, 5 figures, 2 tables, Submitted to ApJL

Despite the importance of AGN in galaxy evolution, accurate AGN identification is often challenging, as common AGN diagnostics can be confused by contributions from star formation and other effects (e.g., Baldwin-Phillips-Terlevich diagrams). However, one promising avenue for identifying AGNs are ``coronal emission lines" (``CLs"), which are highly ionized species of gas with ionization potentials $\ge$ 100 eV. These CLs may serve as excellent signatures for the strong ionizing continuum of AGN. To determine if CLs are in fact strong AGN tracers, we assemble and analyze the largest catalog of optical CL galaxies using the Sloan Digital Sky Survey's Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) catalog. We detect CL emission in 71 MaNGA galaxies, out of the 10,010 unique galaxies from the final MaNGA catalog, with $\ge$ 5$\sigma$ confidence. In our sample, we measure [NeV]$\lambda$3347, $\lambda$3427, [FeVII]$\lambda$3586, $\lambda$3760, $\lambda$6086, and [FeX]$\lambda$6374 emission and crossmatch the CL galaxies with a catalog of AGNs that were confirmed with broad line, X-ray, IR, and radio observations. We find that [NeV] emission, compared to [FeVII] and [FeX] emission, is best at identifying high luminosity AGN. Moreover, we find that the CL galaxies with the least dust extinction yield the most iron CL detections. We posit that the bulk of the iron CLs are destroyed by dust grains in the galaxies with the highest [OIII] luminosities in our sample, and that AGN in the galaxies with low [OIII] luminosities are possibly too weak to be detected using traditional techniques., Comment: 21 pages, 6 figures, 8 tables

This paper presents our solution for the Real Robot Challenge (RRC) III, a competition featured in the NeurIPS 2022 Competition Track, aimed at addressing dexterous robotic manipulation tasks through learning from pre-collected offline data. Participants were provided with two types of datasets for each task: expert and mixed datasets with varying skill levels. While the simplest offline policy learning algorithm, Behavioral Cloning (BC), performed remarkably well when trained on expert datasets, it outperformed even the most advanced offline reinforcement learning (RL) algorithms. However, BC's performance deteriorated when applied to mixed datasets, and the performance of offline RL algorithms was also unsatisfactory. Upon examining the mixed datasets, we observed that they contained a significant amount of expert data, although this data was unlabeled. To address this issue, we proposed a semi-supervised learning-based classifier to identify the underlying expert behavior within mixed datasets, effectively isolating the expert data. To further enhance BC's performance, we leveraged the geometric symmetry of the RRC arena to augment the training dataset through mathematical transformations. In the end, our submission surpassed that of all other participants, even those who employed complex offline RL algorithms and intricate data processing and feature engineering techniques.

Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems. However, available datasets are typically of mixed quality, with a limited number of the trajectories that we would consider as positive examples; i.e., high-quality demonstrations. Therefore, we propose a novel iterative learning algorithm for identifying expert trajectories in unlabeled mixed-quality robotics datasets given a minimal set of positive examples, surpassing existing algorithms in terms of accuracy. We show that applying behavioral cloning to the resulting filtered dataset outperforms several competitive offline reinforcement learning and imitation learning baselines. We perform experiments on a range of simulated locomotion tasks and on two challenging manipulation tasks on a real robotic system; in these experiments, our method showcases state-of-the-art performance. Our website: \url{https://sites.google.com/view/offline-policy-learning-pubc}.

We present multi-wavelength high-spatial resolution (~0.1'', 70 pc) observations of UGC 4211 at z=0.03474, a late-stage major galaxy merger at the closest nuclear separation yet found in near-IR imaging (0.32'', ~230 pc projected separation). Using Hubble Space Telescope/STIS, VLT/MUSE+AO, Keck/OSIRIS+AO spectroscopy, and ALMA observations, we show that the spatial distribution, optical and NIR emission lines, and millimeter continuum emission are all consistent with both nuclei being powered by accreting supermassive black holes (SMBHs). Our data, combined with common black hole mass prescriptions, suggests that both SMBHs have similar masses, log MBH~8.1 (south) and log MBH~8.3 (north), respectively. The projected separation of 230 pc (~6X the black hole sphere of influence) represents the closest-separation dual AGN studied to date with multi-wavelength resolved spectroscopy and shows the potential of nuclear (<50 pc) continuum observations with ALMA to discover hidden growing SMBH pairs. While the exact occurrence rate of close-separation dual AGN is not yet known, it may be surprisingly high, given that UGC 4211 was found within a small, volume-limited sample of nearby hard X-ray detected AGN. Observations of dual SMBH binaries in the sub-kpc regime at the final stages of dynamical friction provide important constraints for future gravitational wave observatories., Comment: 19 pages, 7 figures, 2 tables, published in ApJL

End-to-end reinforcement learning techniques are among the most successful methods for robotic manipulation tasks. However, the training time required to find a good policy capable of solving complex tasks is prohibitively large. Therefore, depending on the computing resources available, it might not be feasible to use such techniques. The use of domain knowledge to decompose manipulation tasks into primitive skills, to be performed in sequence, could reduce the overall complexity of the learning problem, and hence reduce the amount of training required to achieve dexterity. In this paper, we propose the use of Davenport chained rotations to decompose complex 3D rotation goals into a concatenation of a smaller set of more simple rotation skills. State-of-the-art reinforcement-learning-based methods can then be trained using less overall simulated experience. We compare its performance with the popular Hindsight Experience Replay method, trained in an end-to-end fashion using the same amount of experience in a simulated robotic hand environment. Despite a general decrease in performance of the primitive skills when being sequentially executed, we find that decomposing arbitrary 3D rotations into elementary rotations is beneficial when computing resources are limited, obtaining increases of success rates of approximately 10% on the most complex 3D rotations with respect to the success rates obtained by HER trained in an end-to-end fashion, and increases of success rates between 20% and 40% on the most simple rotations., Comment: 5 pages, 3 figures, 3 tables, submitted to ICARA 2023

Robotic manipulation and control has increased in importance in recent years. However, state of the art techniques still have limitations when required to operate in real world applications. This paper explores Hindsight Experience Replay both in simulated and real environments, highlighting its weaknesses and proposing reinforcement-learning based alternatives based on reward and goal shaping. Additionally, several research questions are identified along with potential research directions that could be explored to tackle those questions., Comment: 4 pages, 1 figure, Submitted to PhD Workshop submission at IRC 2022. Updated figure 1

We present James Webb Space Telescope (JWST) Mid-InfraRed Instrument (MIRI) integral-field spectroscopy of the nearby merging, luminous infrared galaxy, NGC 7469. This galaxy hosts a Seyfert type-1.5 nucleus, a highly ionized outflow, and a bright, circumnuclear star-forming ring, making it an ideal target to study AGN feedback in the local Universe. We take advantage of the high spatial/spectral resolution of JWST/MIRI to isolate the star-forming regions surrounding the central active nucleus and study the properties of the dust and warm molecular gas on ~100 pc scales. The starburst ring exhibits prominent Polycyclic Aromatic Hydrocarbon (PAH) emission, with grain sizes and ionization states varying by only ~30%, and a total star formation rate of $\rm 10 - 30 \ M_\odot$/yr derived from fine structure and recombination emission lines. Using pure rotational lines of H2, we detect 1.2$\times$10$^{7} \rm \ M_\odot$ of warm molecular gas at a temperature higher than 200 K in the ring. All PAH bands get significantly weaker towards the central source, where larger and possibly more ionized grains dominate the emission. However, the bulk of the dust and molecular gas in the ring appears unaffected by the ionizing radiation or the outflowing wind from the AGN. These observations highlight the power of JWST to probe the inner regions of dusty, rapidly evolving galaxies for signatures of feedback and inform models that seek to explain the co-evolution of supermassive black holes and their hosts., Comment: 13 pages, 4 figures, 2 tables, Submitted to ApJL

The nearby, luminous infrared galaxy (LIRG) NGC 7469 hosts a Seyfert nucleus with a circumnuclear star-forming ring and is thus the ideal local laboratory for investigating the starburst--AGN connection in detail. We present integral-field observations of the central 1.3 kpc region in NGC 7469 obtained with the JWST Mid-InfraRed Instrument. Molecular and ionized gas distributions and kinematics at a resolution of {\sim}100 pc over the 4.9 - 7.6{\mu}m region are examined to study gas dynamics influenced by the central AGN. The low-ionization [Fe II] {\lambda}5.34{\mu}m and [Ar II] {\lambda}6.99{\mu}m lines are bright on the nucleus and in the starburst ring, as opposed to H2 S(5) {\lambda}6.91{\mu}m which is strongly peaked at the center and surrounding ISM. The high-ionization [Mg V] line is resolved and shows a broad, blueshifted component associated with the outflow. It has a nearly face-on geometry that is strongly peaked on the nucleus, where it reaches a maximum velocity of -650 km/s, and extends about 400 pc to the East. Regions of enhanced velocity dispersion in H2 and [Fe II] {\sim}180 pc from the AGN that also show high L(H2)/L(PAH) and L([Fe II])/L(Pf{\alpha}) ratios to the W and N of the nucleus pinpoint regions where the ionized outflow is depositing energy, via shocks, into the dense interstellar medium between the nucleus and the starburst ring. These resolved mid-infrared observations of the nuclear gas dynamics demonstrate the power of JWST and its high-sensitivity integral-field spectroscopic capability to resolve feedback processes around supermassive black holes in the dusty cores of nearby LIRGs., Comment: 14 pages, 6 figures, 1 table; accepted for publication in ApJL

The existence of life is one of the most fundamental problems of astrophysics. The intriguing existence of progressively complex and apparently improbable living beings should be a general tendency of life in the Universe. We are looking for general physical laws governing the growth of complexity in any astrophysical environment. We posit the existence of a vital scalar field. This scalar is sensitive to the gradient of the inverse of specific entropy, such that its distribution tends to very high values in the interior of living beings. Besides the classical mutations, vital field driven mutations only produce decrements of entropy. The field equations give rise to the existence of vital waves. This theory is able to deal with both the origin of life and the evolution of life. We show that the growth of complexity is accelerated by the vital field., Comment: 19 pages, 3 figures. Accepted for publication in Revista Mexicana de Astronom\'ia y Astrof\'isica

This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipulation task. The RRC consisted of using a TriFinger robot to manipulate a cube along a specified positional trajectory, but with no requirement for the cube to have any specific orientation. We used a relatively simple reward function, a combination of goal-based sparse reward and distance reward, in conjunction with Hindsight Experience Replay (HER) to guide the learning of the DRL agent (Deep Deterministic Policy Gradient (DDPG)). Our approach allowed our agents to acquire dexterous robotic manipulation strategies in simulation. These strategies were then applied to the real robot and outperformed all other competition submissions, including those using more traditional robotic control techniques, in the final evaluation stage of the RRC. Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory. The requirement to also orient the cube makes the agent unable to learn the task through blind exploration due to increased problem complexity. To circumvent this issue, we make novel use of a Knowledge Transfer (KT) technique that allows the strategies learned by the agent in the original task (which was agnostic to cube orientation) to be transferred to this task (where orientation matters). KT allowed the agent to learn and perform the extended task in the simulator, which improved the average positional deviation from 0.134 m to 0.02 m, and average orientation deviation from 142{\deg} to 76{\deg} during evaluation. This KT concept shows good generalisation properties and could be applied to any actor-critic learning algorithm., Comment: This paper has been summited to Expert Systems: the Journal of Knowledge Engineering for reviewing. arXiv admin note: text overlap with arXiv:2109.15233