A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Max entropy exploration guides reinforcement learning agents to pursue achievable goals.
Deep reinforcement learning has recently gained a focus on problems where policy or value functions are independent of goals. Evidence exists that the sampling of goals has a strong effect on the learning performance, but there is a lack of general mechanisms that focus on optimizing the goal sampling process. In this …
Deep RL optimizes goal-based investing strategies.
Goal-conditioned policies are used in order to break down complex reinforcement learning (RL) problems by using subgoals, which can be defined either in state space or in a latent feature space. This can increase the efficiency of learning by using a curriculum, and also enables simultaneous learning and generalization…
GARIM theory explains how conscious manipulation of internal representations enhances goal-directed behavior.
Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior. However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based algorithms use dynamics models and reward functions to achieve goals. Yet, reward functions that evoke desirable behavior are often difficul…
Study optimal portfolio for households with two goals: random and fixed deadlines.
In Multi-Goal Reinforcement Learning, an agent learns to achieve multiple goals with a goal-conditioned policy. During learning, the agent first collects the trajectories into a replay buffer, and later these trajectories are selected randomly for replay. However, the achieved goals in the replay buffer are often biase…
New method learns optimal environment and goal difficulty for reinforcement learning.
Agent learns goals and rewards through language and curiosity.
New approach to goal-based investing using hedging and reinforcement learning.
A method for setting up an automatic curriculum for reinforcement learning tasks.
Financial institutions use LSTM models to predict customer goals.
Intrinsically motivated goal exploration processes enable agents to autonomously sample goals to explore efficiently complex environments with high-dimensional continuous actions. They have been applied successfully to real world robots to discover repertoires of policies producing a wide diversity of effects. Often th…
Many AI problems, in robotics and other domains, are goal-directed, essentially seeking a trajectory leading to some goal state. In such problems, the way we choose to represent a trajectory underlies algorithms for trajectory prediction and optimization. Interestingly, most all prior work in imitation and reinforcemen…
Improves RL planning by proposing sub-goals hierarchically.
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to provide the requisite level of generality, these skills must handle raw sensory input such as images. In this paper, we propose an algo…
DAGR improves navigation by refining goal representations conditioned on the current state.
Sparse reward problems are one of the biggest challenges in Reinforcement Learning. Goal-directed tasks are one such sparse reward problems where a reward signal is received only when the goal is reached. One promising way to train an agent to perform goal-directed tasks is to use Hindsight Learning approaches. In thes…
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward function limits this repertoire and imposes a manual engineering burden. Self-supervised agents that set their own goals can automate this pr…
Consider mutli-goal tasks that involve static environments and dynamic goals. Examples of such tasks, such as goal-directed navigation and pick-and-place in robotics, abound. Two types of Reinforcement Learning (RL) algorithms are used for such tasks: model-free or model-based. Each of these approaches has limitations.…
A framework for goal-based investing with penalties for fund transfers.
Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a function on the state space. However, the sparsity of such reward definition makes traditional reinforcement learning algorithms very inefficien…
A novel framework uses goal-conditioned reinforcement learning to generate diverse samples.
Hindsight Experience Replay (HER) is a multi-goal reinforcement learning algorithm for sparse reward functions. The algorithm treats every failure as a success for an alternative (virtual) goal that has been achieved in the episode. Virtual goals are randomly selected, irrespective of which are most instructive for the…
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient explo…
Eikonal-Constrained QRL improves goal-reaching in reinforcement learning.
A new EM framework for goal-conditioned RL improves performance on sparse reward tasks.
C-Learning estimates reachability over time to solve multi-goal tasks.
Reinforcement learning algorithms use correlations between policies and rewards to improve agent performance. But in dynamic or sparsely rewarding environments these correlations are often too small, or rewarding events are too infrequent to make learning feasible. Human education instead relies on curricula--the break…
OptiGAN uses GAN and RL to optimize sequence generation for specific goals.
We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability. We utilize the variational intrinsic control framework (Gregor et.al., 2016) which maximizes empowerment -- the ability to reliably reach a diverse set of states and show how to ide…
All-goals updating exploits the off-policy nature of Q-learning to update all possible goals an agent could have from each transition in the world, and was introduced into Reinforcement Learning (RL) by Kaelbling (1993). In prior work this was mostly explored in small-state RL problems that allowed tabular representati…
This work tackles long-term visual planning by goal-conditioned hierarchical predictors.
Investor aims to meet financial goals with deadlines and target amounts, considering stock trading costs.
PlanGAN uses GANs to plan efficient trajectories for multi-goal tasks in sparse reward environments.
Physics-informed GCRL tackles sparse feedback learning with hybrid dynamics.
Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failed experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and un…
AMIGo uses adversarial intrinsic goals to teach RL agents new skills.
Paper proposes a method to learn goal-reaching behaviors from scratch using imitation learning.
This paper proposes a method to learn from expert trajectories by decomposing tasks into sub-goals.
GOIMDA selects inputs to maximize expected influence on a goal functional, reducing data acquisition needs.
This work improves imitation learning and goal-conditioned RL by estimating value densities.
A novel approach learns goal-conditioned policies for locomotion using batch RL.
Study combines chit-chat and goal-oriented dialogue in fantasy games.
Generative neural nets learn deep policies conditioned on goals.
Unified algorithm tackles various RL goals like reward-free and preference-based learning.
The automatic and efficient discovery of skills, without supervision, for long-living autonomous agents, remains a challenge of Artificial Intelligence. Intrinsically Motivated Goal Exploration Processes give learning agents a human-inspired mechanism to sequentially select goals to achieve. This approach gives a new p…