Proto Successor Measure: Representing the Space of All Possible Solutions of Reinforcement Learning

• URL: http://arxiv.org/abs/2411.19418v1
• Date: Fri, 29 Nov 2024 00:09:39 GMT
• Title: Proto Successor Measure: Representing the Space of All Possible Solutions of Reinforcement Learning
• Authors: Siddhant Agarwal, Harshit Sikchi, Peter Stone, Amy Zhang,
• Abstract summary: "Zero-shot learning" is elusive for general-purpose reinforcement learning algorithms. We present emphProto Successor Measure: the basis set for all possible solutions of Reinforcement Learning. We derive a practical algorithm to learn these basis functions using only interaction data from the environment.
• Score: 37.55496993803242
• License:
• Abstract: Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment. Referred to as "zero-shot learning," this ability remains elusive for general-purpose reinforcement learning algorithms. While recent works have attempted to produce zero-shot RL agents, they make assumptions about the nature of the tasks or the structure of the MDP. We present \emph{Proto Successor Measure}: the basis set for all possible solutions of Reinforcement Learning in a dynamical system. We provably show that any possible policy can be represented using an affine combination of these policy independent basis functions. Given a reward function at test time, we simply need to find the right set of linear weights to combine these basis corresponding to the optimal policy. We derive a practical algorithm to learn these basis functions using only interaction data from the environment and show that our approach can produce the optimal policy at test time for any given reward function without additional environmental interactions. Project page: https://agarwalsiddhant10.github.io/projects/psm.html.

Related papers

• Learning Optimal Deterministic Policies with Stochastic Policy Gradients [62.81324245896716]
  Policy gradient (PG) methods are successful approaches to deal with continuous reinforcement learning (RL) problems. In common practice, convergence (hyper)policies are learned only to deploy their deterministic version. We show how to tune the exploration level used for learning to optimize the trade-off between the sample complexity and the performance of the deployed deterministic policy.
  arXiv  Detail & Related papers  (2024-05-03T16:45:15Z)
• Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization [17.729842629392742]
  We study a Reinforcement Learning problem in which we are given a set of trajectories collected with K baseline policies. The goal is to learn a policy which performs as well as the best combination of baselines on the entire state space.
  arXiv  Detail & Related papers  (2024-03-28T14:34:02Z)
• Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer [7.970144204429356]
  We introduce an SF-based extension of the Optimistic Linear Support algorithm to learn a set of policies whose SFs form a convex coverage set. We prove that policies in this set can be combined via generalized policy improvement to construct optimal behaviors for any new linearly-expressible tasks.
  arXiv  Detail & Related papers  (2022-06-22T19:00:08Z)
• Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning [20.85397773933171]
  We propose a novel option indexing approach to hierarchical learning (OI-HRL) This allows us to effectively reuse a large library of pretrained options, in zero-shot generalization at test time. We develop a meta-training loop that learns the representations of options and environments over a series of HRL problems.
  arXiv  Detail & Related papers  (2022-06-12T14:39:02Z)
• Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates [63.58053355357644]
  We study the problem of learning a good set of policies, so that when combined together, they can solve a wide variety of unseen reinforcement learning tasks. We show theoretically that having access to a specific set of diverse policies, which we call a set of independent policies, can allow for instantaneously achieving high-level performance.
  arXiv  Detail & Related papers  (2021-12-30T12:20:46Z)
• Efficient Local Planning with Linear Function Approximation [27.90696655434707]
  We study query and computationally efficient planning algorithms with linear function approximation and a simulator. We propose an algorithm named confident Monte Carlo least square policy iteration (Confident MC-LSPI) for this setting. One technical contribution of our work is the introduction of a novel proof technique that makes use of a virtual policy algorithm.
  arXiv  Detail & Related papers  (2021-08-12T04:56:33Z)
• PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning [102.36450942613091]
  We propose an inverse reinforcement learning algorithm, called emphinverse temporal difference learning (ITD) We show how to seamlessly integrate ITD with learning from online environment interactions, arriving at a novel algorithm for reinforcement learning with demonstrations, called $Psi Phi$-learning.
  arXiv  Detail & Related papers  (2021-02-24T21:12:09Z)
• Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration [143.43658264904863]
  We show how iteration under a more standard notion of low inherent Bellman error, typically employed in least-square value-style algorithms, can provide strong PAC guarantees on learning a near optimal value function. We present a computationally tractable algorithm for the reward-free setting and show how it can be used to learn a near optimal policy for any (linear) reward function.
  arXiv  Detail & Related papers  (2020-08-18T04:34:21Z)
• Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling [126.69933134648541]
  We present a meta-reinforcement learning algorithm that is both efficient and extrapolates well when faced with out-of-distribution tasks at test time. Our method is based on a simple insight: we recognize that dynamics models can be adapted efficiently and consistently with off-policy data.
  arXiv  Detail & Related papers  (2020-06-12T13:34:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.