Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization

• URL: http://arxiv.org/abs/2011.08541v1
• Date: Tue, 17 Nov 2020 10:17:45 GMT
• Title: Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
• Authors: Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh
• Abstract summary: inverse reinforcement learning (IRL) is relevant to a variety of tasks including value alignment and robot learning from demonstration. This paper presents an IRL framework called Bayesian optimization-IRL (BO-IRL) which identifies multiple solutions consistent with the expert demonstrations.
• Score: 43.51553742077343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The problem of inverse reinforcement learning (IRL) is relevant to a variety of tasks including value alignment and robot learning from demonstration. Despite significant algorithmic contributions in recent years, IRL remains an ill-posed problem at its core; multiple reward functions coincide with the observed behavior and the actual reward function is not identifiable without prior knowledge or supplementary information. This paper presents an IRL framework called Bayesian optimization-IRL (BO-IRL) which identifies multiple solutions that are consistent with the expert demonstrations by efficiently exploring the reward function space. BO-IRL achieves this by utilizing Bayesian Optimization along with our newly proposed kernel that (a) projects the parameters of policy invariant reward functions to a single point in a latent space and (b) ensures nearby points in the latent space correspond to reward functions yielding similar likelihoods. This projection allows the use of standard stationary kernels in the latent space to capture the correlations present across the reward function space. Empirical results on synthetic and real-world environments (model-free and model-based) show that BO-IRL discovers multiple reward functions while minimizing the number of expensive exact policy optimizations.

Related papers

• REBEL: A Regularization-Based Solution for Reward Overoptimization in Robotic Reinforcement Learning from Human Feedback [61.54791065013767]
  A misalignment between the reward function and user intentions, values, or social norms can be catastrophic in the real world. Current methods to mitigate this misalignment work by learning reward functions from human preferences. We propose a novel concept of reward regularization within the robotic RLHF framework.
  arXiv  Detail & Related papers  (2023-12-22T04:56:37Z)
• A Novel Variational Lower Bound for Inverse Reinforcement Learning [5.370126167091961]
  Inverse reinforcement learning (IRL) seeks to learn the reward function from expert trajectories. We present a new Variational Lower Bound for IRL (VLB-IRL) Our method simultaneously learns the reward function and policy under the learned reward function.
  arXiv  Detail & Related papers  (2023-11-07T03:50:43Z)
• Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees [56.848265937921354]
  Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy. Many algorithms for IRL have an inherently nested structure. We develop a novel single-loop algorithm for IRL that does not compromise reward estimation accuracy.
  arXiv  Detail & Related papers  (2022-10-04T17:13:45Z)
• Invariance in Policy Optimisation and Partial Identifiability in Reward Learning [67.4640841144101]
  We characterise the partial identifiability of the reward function given popular reward learning data sources. We also analyse the impact of this partial identifiability for several downstream tasks, such as policy optimisation.
  arXiv  Detail & Related papers  (2022-03-14T20:19:15Z)
• Dynamics-Aware Comparison of Learned Reward Functions [21.159457412742356]
  The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world. Reward functions are typically compared by considering the behavior of optimized policies, but this approach conflates deficiencies in the reward function with those of the policy search algorithm used to optimize it. We propose the Dynamics-Aware Reward Distance (DARD), a new reward pseudometric.
  arXiv  Detail & Related papers  (2022-01-25T03:48:00Z)
• On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game [140.19656665344917]
  We study the reward-free RL problem, where an agent aims to thoroughly explore the environment without any pre-specified reward function. We tackle this problem under the context of function approximation, leveraging powerful function approximators. We establish the first provably efficient reward-free RL algorithm with kernel and neural function approximators.
  arXiv  Detail & Related papers  (2021-10-19T07:26:33Z)
• Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning [22.242379207077217]
  We show how to show the reward function's code to the RL agent so it can exploit the function's internal structure to learn optimal policies. First, we propose reward machines, a type of finite state machine that supports the specification of reward functions. We then describe different methodologies to exploit this structure to support learning, including automated reward shaping, task decomposition, and counterfactual reasoning with off-policy learning.
  arXiv  Detail & Related papers  (2020-10-06T00:10:16Z)
• 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)

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.