bash setup-v2.sheach time on openning the gitpod ssh link.- use the command
ssh -L 16006:127.0.0.1:6006 'kangoxford-densepacking-yky9rdbuiid@kangoxford-densepacking-yky9rdbuiid.ssh.ws-eu61.gitpod.io'if you want to run in vscode - use this link to get login via gitpod
https://kangoxford-densepacking-yky9rdbuiid.ws-eu61.gitpod.io - Colab Files
- Consideration of risk in reinforcement learning, Paper, Not Find Code, (Accepted by ICML 1994)
- Multi-criteria Reinforcement Learning, Paper, Not Find Code, (Accepted by ICML 1998)
- Lyapunov design for safe reinforcement learning, Paper, Not Find Code, (Accepted by ICML 2002)
- Risk-sensitive reinforcement learning, Paper, Not Find Code, (Accepted by Machine Learning, 2002)
- Risk-Sensitive Reinforcement Learning Applied to Control under Constraints, Paper, Not Find Code, (Accepted by Journal of Artificial Intelligence Research, 2005)
- An actor-critic algorithm for constrained markov decision processes, Paper, Not Find Code, (Accepted by Systems & Control Letters, 2005)
- Reinforcement learning for MDPs with constraints, Paper, Not Find Code, (Accepted by European Conference on Machine Learning 2006)
- Discounted Markov decision processes with utility constraints, Paper, Not Find Code, (Accepted by Computers & Mathematics with Applications, 2006)
- Constrained reinforcement learning from intrinsic and extrinsic rewards, Paper, Not Find Code, (Accepted by International Conference on Development and Learning 2007)
- Safe exploration for reinforcement learning, Paper, Not Find Code, (Accepted by ESANN 2008)
- Percentile optimization for Markov decision processes with parameter uncertainty, Paper, Not Find Code, (Accepted by Operations research, 2010)
- Probabilistic goal Markov decision processes, Paper, Not Find Code, (Accepted by AAAI 2011)
- Safe reinforcement learning in high-risk tasks through policy improvement, Paper, Not Find Code, (Accepted by IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) 2011)
- Safe Exploration in Markov Decision Processes, Paper, Not Find Code, (Accepted by ICML 2012)
- Policy gradients with variance related risk criteria, Paper, Not Find Code, (Accepted by ICML 2012)
- Risk aversion in Markov decision processes via near optimal Chernoff bounds, Paper, Not Find Code, (Accepted by NeurIPS 2012)
- Safe Exploration of State and Action Spaces in Reinforcement Learning, Paper, Not Find Code, (Accepted by Journal of Artificial Intelligence Research, 2012)
- An Online Actor–Critic Algorithm with Function Approximation for Constrained Markov Decision Processes, Paper, Not Find Code, (Accepted by Journal of Optimization Theory and Applications, 2012)
- Safe policy iteration, Paper, Not Find Code, (Accepted by ICML 2013)
- Reachability-based safe learning with Gaussian processes, Paper, Not Find Code (Accepted by IEEE CDC 2014)
- Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret, Paper, Not Find Code, (Accepted by ICML 2015)
- High-Confidence Off-Policy Evaluation, Paper, Code (Accepted by AAAI 2015)
- Safe Exploration for Optimization with Gaussian Processes, Paper, Not Find Code (Accepted by ICML 2015)
- Safe Exploration in Finite Markov Decision Processes with Gaussian Processes, Paper, Not Find Code (Accepted by NeurIPS 2016)
- Safe and efficient off-policy reinforcement learning, Paper, Code (Accepted by NeurIPS 2016)
- Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving, Paper, Not Find Code (only Arxiv, 2016, citation 530+)
- Safe Learning of Regions of Attraction in Uncertain, Nonlinear Systems with Gaussian Processes, Paper, Code (Accepetd by CDC 2016)
- Safety-constrained reinforcement learning for MDPs, Paper, Not Find Code (Accepted by InInternational Conference on Tools and Algorithms for the Construction and Analysis of Systems 2016)
- Convex synthesis of randomized policies for controlled Markov chains with density safety upper bound constraints, Paper, Not Find Code (Accepted by American Control Conference 2016)
- Combating Deep Reinforcement Learning's Sisyphean Curse with Intrinsic Fear, Paper, Not Find Code (only Openreview, 2016)
- Combating reinforcement learning's sisyphean curse with intrinsic fear, Paper, Not Find Code (only Arxiv, 2016)
- Constrained Policy Optimization (CPO), Paper, Code (Accepted by ICML 2017)
- Risk-constrained reinforcement learning with percentile risk criteria, Paper, , Not Find Code (Accepted by The Journal of Machine Learning Research, 2017)
- Probabilistically Safe Policy Transfer, Paper, Not Find Code (Accepted by ICRA 2017)
- Accelerated primal-dual policy optimization for safe reinforcement learning, Paper, Not Find Code (Arxiv, 2017)
- Stagewise safe bayesian optimization with gaussian processes, Paper, Not Find Code (Accepted by ICML 2018)
- Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning, Paper, Code (Accepted by ICLR 2018)
- Safe Model-based Reinforcement Learning with Stability Guarantees, Paper, Code (Accepted by NeurIPS 2018)
- A Lyapunov-based Approach to Safe Reinforcement Learning, Paper, Not Find Code (Accepted by NeurIPS 2018)
- Constrained Cross-Entropy Method for Safe Reinforcement Learning, Paper, Not Find Code (Accepted by NeurIPS 2018)
- Safe Reinforcement Learning via Formal Methods, Paper, Not Find Code (Accepted by AAAI 2018)
- Safe exploration and optimization of constrained mdps using gaussian processes, Paper, Not Find Code (Accepted by AAAI 2018)
- Safe reinforcement learning via shielding, Paper, Code (Accepted by AAAI 2018)
- Trial without Error: Towards Safe Reinforcement Learning via Human Intervention, Paper, Not Find Code (Accepted by AAMAS 2018)
- Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning, Paper, Not Find Code (Accepted by CDC 2018)
- The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems, Paper, Code (Accepted by CoRL 2018)
- OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World, Paper, Not Find Code (Accepted by ICRA 2018)
- Safe reinforcement learning on autonomous vehicles, Paper, Not Find Code (Accepted by IROS 2018)
- Trial without error: Towards safe reinforcement learning via human intervention, Paper, Code (Accepted by AAMAS 2018)
- Safe reinforcement learning: Learning with supervision using a constraint-admissible set, Paper, Not Find Code (Accepted by Annual American Control Conference (ACC) 2018)
- Verification and repair of control policies for safe reinforcement learning, Paper, Not Find Code (Accepted by Applied Intelligence, 2018)
- Safe Exploration in Continuous Action Spaces, Paper, Code, (only Arxiv, 2018, citation 200+)
- Safe exploration of nonlinear dynamical systems: A predictive safety filter for reinforcement learning, Paper, Not Find Code (Arxiv, 2018, citation 40+)
- Batch policy learning under constraints, Paper, Code (Accepted by ICML 2019)
- Safe Policy Improvement with Baseline Bootstrapping, Paper, Not Find Code (Accepted by ICML 2019)
- Convergent Policy Optimization for Safe Reinforcement Learning, Paper, Code (Accepted by NeurIPS 2019)
- Constrained reinforcement learning has zero duality gap, Paper, Not Find Code (Accepted by NeurIPS 2019)
- Reinforcement learning with convex constraints, Paper, Code (Accepted by NeurIPS 2019)
- Reward constrained policy optimization, Paper, Not Find Code (Accepted by ICLR 2019)
- Supervised policy update for deep reinforcement learning, Paper, Code, (Accepted by ICLR 2019)
- Lyapunov-based safe policy optimization for continuous control, Paper, Not Find Code (Accepted by ICML Workshop RL4RealLife 2019)
- Safe reinforcement learning with model uncertainty estimates, Paper, Not Find Code (Accepted by ICRA 2019)
- Safe reinforcement learning with scene decomposition for navigating complex urban environments, Paper, Code, (Accepted by IV 2019)
- Verifiably safe off-model reinforcement learning, Paper, Code (Accepted by InInternational Conference on Tools and Algorithms for the Construction and Analysis of Systems 2019)
- Probabilistic policy reuse for safe reinforcement learning, Paper, Not Find Code, (Accepted by ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2019)
- Projected stochastic primal-dual method for constrained online learning with kernels, Paper, Not Find Code, (Accepted by IEEE Transactions on Signal Processing, 2019)
- Resource constrained deep reinforcement learning, Paper, Not Find Code, (Accepted by 29th International Conference on Automated Planning and Scheduling 2019)
- Temporal logic guided safe reinforcement learning using control barrier functions, Paper, Not Find Code (Arxiv, Citation 25+, 2019)
- Safe policies for reinforcement learning via primal-dual methods, Paper, Not Find Code (Arxiv, Citation 25+, 2019)
- Value constrained model-free continuous control, Paper, Not Find Code (Arxiv, Citation 35+, 2019)
- Safe Reinforcement Learning in Constrained Markov Decision Processes (SNO-MDP), Paper, Code (Accepted by ICML 2020)
- Responsive Safety in Reinforcement Learning by PID Lagrangian Methods, Paper, Code (Accepted by ICML 2020)
- Constrained markov decision processes via backward value functions, Paper, Code (Accepted by ICML 2020)
- Projection-Based Constrained Policy Optimization (PCPO), Paper, Code (Accepted by ICLR 2020)
- First order constrained optimization in policy space (FOCOPS),Paper, Code (Accepted by NeurIPS 2020)
- Safe reinforcement learning via curriculum induction, Paper, Code (Accepted by NeurIPS 2020)
- Constrained episodic reinforcement learning in concave-convex and knapsack settings, Paper, Code (Accepted by NeurIPS 2020)
- Risk-sensitive reinforcement learning: Near-optimal risk-sample tradeoff in regret, Paper, Not Find Code (Accepted by NeurIPS 2020)
- IPO: Interior-point Policy Optimization under Constraints, Paper, Not Find Code (Accepted by AAAI 2020)
- Safe reinforcement learning using robust mpc, Paper, Not Find Code (IEEE Transactions on Automatic Control, 2020)
- Safe reinforcement learning via projection on a safe set: How to achieve optimality?, Paper, Not Find Code (Accepted by IFAC 2020)
- Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning, Paper, Code, (Accepted by International Joint Conference on Neural Networks (IJCNN) 2020)
- Safe reinforcement learning through meta-learned instincts, Paper, Not Find Code (Accepted by The Conference on Artificial Life 2020)
- Learning safe policies with cost-sensitive advantage estimation, Paper, Not Find Code (Openreview 2020)
- Safe reinforcement learning using probabilistic shields, Paper, Not Find Code (2020)
- A constrained reinforcement learning based approach for network slicing, Paper, Not Find Code (Accepted by IEEE 28th International Conference on Network Protocols (ICNP) 2020)
- Exploration-exploitation in constrained mdps, Paper, Not Find Code (Arxiv, 2020)
- Safe reinforcement learning using advantage-based intervention, Paper, Code (Accepted by ICML 2021)
- Shortest-path constrained reinforcement learning for sparse reward tasks, Paper, Code, (Accepted by ICML 2021)
- Density constrained reinforcement learning, Paper, Not Find Code (Accepted by ICML 2021)
- CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee, Paper, Not Find Code (Accepted by ICML 2021)
- Safe Reinforcement Learning by Imagining the Near Future (SMBPO), Paper, Code (Accepted by NeurIPS 2021)
- Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning, Paper, Not Find Code (Accepted by NeurIPS 2021)
- Risk-Sensitive Reinforcement Learning: Symmetry, Asymmetry, and Risk-Sample Tradeoff, Paper, Not Find Code (Accepted by NeurIPS 2021)
- Safe reinforcement learning with natural language constraints, Paper, Code, (Accepted by NeurIPS 2021)
- Learning policies with zero or bounded constraint violation for constrained mdps, Paper, Not Find Code (Accepted by NeurIPS 2021)
- Conservative safety critics for exploration, Paper, Not Find Code (Accepted by ICLR 2021)
- Wcsac: Worst-case soft actor critic for safety-constrained reinforcement learning, Paper, Not Find Code (Accepted by AAAI 2021)
- Risk-averse trust region optimization for reward-volatility reduction, Paper, Not Find Code (Accepted by IJCAI 2021)
- AlwaysSafe: Reinforcement Learning Without Safety Constraint Violations During Training, Paper, Code (Accepted by AAMAS 2021)
- Safe Continuous Control with Constrained Model-Based Policy Optimization (CMBPO), Paper, Code (Accepted by IROS 2021)
- Context-aware safe reinforcement learning for non-stationary environments, Paper, Code (Accepted by ICRA 2021)
- Robot Reinforcement Learning on the Constraint Manifold, Paper, Code (Accepted by CoRL 2021)
- Provably efficient safe exploration via primal-dual policy optimization, Paper, Not Find Code (Accepted by the International Conference on Artificial Intelligence and Statistics 2021)
- Safe model-based reinforcement learning with robust cross-entropy method, Paper, Code (Accepted by ICLR 2021 Workshop on Security and Safety in Machine Learning Systems)
- MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance, Paper, Code (Accepted by Workshop on Safe and Robust Control of Uncertain Systems at NeurIPS 2021)
- Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks, Paper, Code (Accepted by Conference on Learning for Dynamics and Control 2021)
- Can You Trust Your Autonomous Car? Interpretable and Verifiably Safe Reinforcement Learning, Paper, Not Find Code (Accepted by IV 2021)
- Provably safe model-based meta reinforcement learning: An abstraction-based approach, Paper, Not Find Code (Accepted by CDC 2021)
- Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones, Paper, Code, (Accepted by IEEE RAL, 2021)
- Reinforcement learning control of constrained dynamic systems with uniformly ultimate boundedness stability guarantee, Paper, Not Find Code (Accepted by Automatica, 2021)
- A predictive safety filter for learning-based control of constrained nonlinear dynamical systems, Paper, Not Find Code (Accepted by Automatica, 2021)
- A simple reward-free approach to constrained reinforcement learning, Paper, Not Find Code (Arxiv, 2021)
- State augmented constrained reinforcement learning: Overcoming the limitations of learning with rewards, Paper, Not Find Code (Arxiv, 2021)
- DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention, Paper, Not Find Code (Arxiv, 2021)
- Constrained Variational Policy Optimization for Safe Reinforcement Learning, Paper, Code (ICML 2022)
- Stability-Constrained Markov Decision Processes Using MPC, Paper, Not Find Code (Accepted by Automatica, 2022)
- Safe reinforcement learning using robust action governor, Paper, Not Find Code (Accepted by In Learning for Dynamics and Control, 2022)
- A primal-dual approach to constrained markov decision processes, Paper, Not Find Code (Arxiv, 2022)
- SAUTE RL: Almost Surely Safe Reinforcement Learning Using State Augmentation, Paper, Not Find Code (Arxiv, 2022)
- Finding Safe Zones of policies Markov Decision Processes, Paper, Not Find Code (Arxiv, 2022)
- CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning, Paper, Code (Arxiv, 2022)
- SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition, Paper, Not Find Code (Arxiv, 2022)
- Penalized Proximal Policy Optimization for Safe Reinforcement Learning, Paper, Not Find Code (Arxiv, 2022)
- Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning, Paper, Not Find Code (Arxiv, 2022)
- Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs, Paper, Not Find Code (Arxiv, 2022)
- Guided Safe Shooting: model based reinforcement learning with safety constraints, Paper, Not Find Code (Arxiv, 2022)
- Safe Reinforcement Learning via Confidence-Based Filters, Paper, Not Find Code (Arxiv, 2022)
- TRC: Trust Region Conditional Value at Risk for Safe Reinforcement Learning, Paper, Code (Accepted by IEEE RAL, 2022)
- Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Risk, Paper, Not Find Code (Accepted by IEEE RAL, 2022)
- Multi-Agent Constrained Policy Optimisation (MACPO), Paper, Code (Arxiv, 2021)
- MAPPO-Lagrangian, Paper, Code (Arxiv, 2021)
- Decentralized policy gradient descent ascent for safe multi-agent reinforcement learning, Paper, Not Find Code (Accepted by AAAI 2021)
- Safe multi-agent reinforcement learning via shielding, Paper, Not Find Code (Accepted by AAMAS 2021)
- CMIX: Deep Multi-agent Reinforcement Learning with Peak and Average Constraints, Paper, Not Find Code (Accepted by Joint European Conference on Machine Learning and Knowledge Discovery in Databases 2021)
- Safe-Reinforcement-Learning-Baselines
- Almost Surely Safe RL Using State Augmentation
- PPO Lagrangian in Pytorch
A packing P is defined as a collection of non-overlapping (i.e., hard) objects or particles in either a finite-sized container or d-dimensional Euclidean space R^d. The packing density \fai is defined as the fraction of space R^d covered by the particles. A problem that has been a source of fascination to mathematicians and scientists for centuries is the determination of the densest arrangement(s) of particles that do not tile space and the associated maximal density \fai_max.
Since the well-known Kepler’s conjecture in 1611 concerned with the densest sphere packing, it took nearly 400 years for this problem to be rigorously proved. This celebrated question was further highlighted by David Hilbert as the 18th problem, i.e., how one can arrange most densely in space an infinite number of equal solids of given form, hoping to guide mathematical research in the twentieth century. There have been many other attempts concerning optimal packings while remaining unsolved, of which we pay particular attention to Ulam’s conjecture stating that congruent spheres have the lowest optimal packing density of all convex bodies in R^3. Proving optimality in many 3D packing problems is surprisingly difficult. Comparatively much less is known about dense packings of hard non-spherical particle systems. Since non-spherical particles have both translational and rotational degrees of freedom, they usually have a richer phase diagram than spheres, i.e., the former can possess different degrees of translational and orientational order.
- It is important to introduce the lattice packing composed of nonoverlapping identical particles centered at the points of a lattice \Lambda with a common orientation, which, for an ordered packing, possibly corresponds to the maximally dense packings.
-
In 2004, Donev et al. [30] proposed a simple monoclinic crystal with two ellipsoids of different orientations per unit cell (SM2).
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It was only recently that an unusual family of crystalline packings of biaxial ellipsoids was discovered, which are denser than the corresponding SM2 packings for a specific range of aspect ratios (like self-dual ellipsoids with 1.365<\alpha<1.5625.
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Can denser packing been discovered via via a reinforcenment learning–based strategy?
To construct possible packing, we consider cases with repeating unit cell, which contains N parrtticles. The cell's lattice repetition is governed by the translation vectors, subject to the constraint that no two particle overlap.
- The number of particles N is small, typically N < 12.
- The three vectors that span the simulation cell are allowed to vary independently of each other in both their length and orientation.
- We do not make any assumptions concerning the orientation of the box here. A commonly used choice for variable-box-shape simulations is to have one of the box vectors along the x axis, another vector in the positive part of the xy-plane, and the third in the z > 0 half space.
The adaptive shrinking cell scheme is based on the standard Monte Carlo (MC) method, where the arbitrarily chosen particle is given random translation and rotation. The main improvement is that the adaptive shrinking cell scheme allows for deformation (compression/expansion) of the fundamental cell, leading to a higher packing density. During the procedure, a trial is rejected if any two particles overlap; otherwise, the trial is accepted.
- Initial configurations: random, dilute, and without overlap (sometimes start from certain dilute packings).
- Particle trial move(translation + rotation ): based on Metropolis acceptance (with no overlap), 1e3 momvements averagely per particle. The probabilitiies (sum=1) of translation and rotation are also controlled variables.
- Cell trial move (see Choice.02 in action design space): after the step of particle trial move. All particles will move correspondingly in this procedure. Cell trial move will be accepted when no overlap detected.
- Conduct step2 and step3 repeatedly until the system can be compressed no more.
We firstly concentrate on a subproblem in which particles are fixed (both centroid and orientation) with adaptive cell.
- Objective function: the volume of cell (should be minized)
- Action space (12 variables): three sets of euler angle (for the rotation of cell vectors) + vector lengths
- Observation space: particle info (scaled coordinate + quaternion + aspherical shape) + three cell vectors
- (!!!) Reward function: The reduction in the volume of cell could possibly arisen from increasing overlap in the packing.
- Choice.01
Uniform scaling (volume change) & deformation (shape change).
Then what is deformation?
- Choice.02
Add a small strain of the fundamental cell, including both volume and shape changes.
This choice can be seen as the generalized form of Choice.01.
- Choice.03
Here we employ random rotation on three vectors of the fundamental cell, and set their lengths as random variables.
Implemented via pymoo.
