arXiv research

This paper presents a unifying framework for reinforcement learning and planning.

Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may …

Survey of integrating planning and learning in model-based reinforcement learning.

Vanguard uses AI to create personalized financial plans.

Efficient algorithms for planning in cooperative multi-agent reinforcement learning with combinatorial action spaces.

This work defines a complexity measure for BAMDP planning and introduces state abstraction for more efficient approximate planning.

PBCS combines RL and motion planning for better exploration.

The paper explores how planning with models improves credit assignment in reinforcement learning.

RL enhances LLM planning but introduces spurious solutions and diversity collapse.

Selective planning with imperfect models reduces harmful effects of model inadequacy.

This paper bridges Markowitz planning and deep reinforcement learning for portfolio optimization.

Improves RL planning by proposing sub-goals hierarchically.

Deep RL drone trained to compete against classical path planning in drone racing.

New method learns state embeddings from demonstrations for improved reinforcement learning.

Study shows optimal RL with transition look-ahead is NP-hard for $\ell \geq 2$.

In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle. We study the effectiveness of the near-optimal cost-to-go oracle on the planning horizon and demonstrate that the cost-to-go oracle shortens the learner's planning horizon as function of its accurac…

The paper introduces affordances for reinforcement learning, improving planning and learning efficiency.

One of the key challenges in applying reinforcement learning to real-life problems is that the amount of train-and-error required to learn a good policy increases drastically as the task becomes complex. One potential solution to this problem is to combine reinforcement learning with automated symbol planning and utili…

We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to inter…

State-of-the-art efficient model-based Reinforcement Learning (RL) algorithms typically act by iteratively solving empirical models, i.e., by performing \emph{full-planning} on Markov Decision Processes (MDPs) built by the gathered experience. In this paper, we focus on model-based RL in the finite-state finite-horizon…

Paper tackles continual reinforcement learning by forgetting, proposing a planning method with online world models.

Plan2Explore learns new tasks efficiently through self-supervised planning.

The explore{exploit dilemma is one of the central challenges in Reinforcement Learning (RL). Bayesian RL solves the dilemma by providing the agent with information in the form of a prior distribution over environments; however, full Bayesian planning is intractable. Planning with the mean MDP is a common myopic approxi…

Plan2Vec learns image representations without labels, improving control tasks.

Motion planning is an essential component in most of today's robotic applications. In this work, we consider the learning setting, where a set of solved motion planning problems is used to improve the efficiency of motion planning on different, yet similar problems. This setting is important in applications with rapidl…

Graph neural network executes value iteration for flexible environments.

Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning. However, predictive uncertainties---especially ones derived from modern deep learning systems---can be inaccurate and impose a bottleneck on performance. This paper explores which uncertainties are needed for…

Planning methods can solve temporally extended sequential decision making problems by composing simple behaviors. However, planning requires suitable abstractions for the states and transitions, which typically need to be designed by hand. In contrast, model-free reinforcement learning (RL) can acquire behaviors from l…

Deep reinforcement learning has learned to play many games well, but failed on others. To better characterize the modes and reasons of failure of deep reinforcement learners, we test the widely used Asynchronous Actor-Critic (A2C) algorithm on four deceptive games, which are specially designed to provide challenges to …

Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate. An algorithm should ideally be able to trust an imperfect model over a reasonably long planning horizon, and only rely on model-free updates when the …

We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiab…

Most microscopic pedestrian navigation models use the concept of "forces" applied to the pedestrian agents to replicate the navigation environment. While the approach could provide believable results in regular situations, it does not always resemble natural pedestrian navigation behaviour in many typical settings. In …

New algorithm improves reinforcement learning from partial observations.

In reinforcement learning, the discount factor $γ$ controls the agent's effective planning horizon. Traditionally, this parameter was considered part of the MDP; however, as deep reinforcement learning algorithms tend to become unstable when the effective planning horizon is long, recent works refer to $γ$ as a hyper-p…

The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling. We …

Improved CEM for fast real-time planning in high-dimensional control tasks.

We introduce Dynamic Planning Networks (DPN), a novel architecture for deep reinforcement learning, that combines model-based and model-free aspects for online planning. Our architecture learns to dynamically construct plans using a learned state-transition model by selecting and traversing between simulated states and…

Model-Based Offline Planning (MBOP) learns models from offline data to control systems directly.

This research introduces an autonomous robot navigation method using reinforcement learning.

ERP improves drug discovery by balancing molecule generation quality and efficiency.

Computer-assisted synthesis planning aims to help chemists find better reaction pathways faster. Finding viable and short pathways from sugar molecules to value-added chemicals can be modeled as a retrosynthesis planning problem with a catalyst allowed. This is a crucial step in efficient biomass conversion. The tradit…

In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably intertwined. If the model is not able to provide sensible long-term prediction, the executed planner would exploit model flaws, which can yield catastrophic failures. This paper focuse…

Unified view on selective credit assignment for reinforcement learning.

Paper tackles efficient navigation in constrained environments using supervised and reinforcement learning.

We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction. We call this framework Planning with Abstract Learned Models (PALM). By representing subtasks symbolically using a n…

Integrates skills and world models for efficient task solving and transfer.

Algorithm learns multiple tasks with minimal planning, achieving near-optimal performance.

Reinforcement learning for optimizing retirement plans and target dated funds.