Self-Supervised Online Reward Shaping in Sparse-Reward Environments
- URL: http://arxiv.org/abs/2103.04529v1
- Date: Mon, 8 Mar 2021 03:28:04 GMT
- Title: Self-Supervised Online Reward Shaping in Sparse-Reward Environments
- Authors: Farzan Memarian, Wonjoon Goo, Rudolf Lioutikov, Ufuk Topcu, and Scott
Niekum
- Abstract summary: We propose a novel reinforcement learning framework that performs self-supervised online reward shaping.
The proposed framework alternates between updating a policy and inferring a reward function.
Experimental results on several sparse-reward environments demonstrate that the proposed algorithm is significantly more sample efficient than the state-of-the-art baseline.
- Score: 36.01839934355542
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a novel reinforcement learning framework that performs
self-supervised online reward shaping, yielding faster, sample efficient
performance in sparse reward environments. The proposed framework alternates
between updating a policy and inferring a reward function. While the policy
update is done with the inferred, potentially dense reward function, the
original sparse reward is used to provide a self-supervisory signal for the
reward update by serving as an ordering over the observed trajectories. The
proposed framework is based on the theory that altering the reward function
does not affect the optimal policy of the original MDP as long as we maintain
certain relations between the altered and the original reward. We name the
proposed framework \textit{ClAssification-based REward Shaping} (CaReS), since
we learn the altered reward in a self-supervised manner using classifier based
reward inference. Experimental results on several sparse-reward environments
demonstrate that the proposed algorithm is not only significantly more sample
efficient than the state-of-the-art baseline, but also achieves a similar
sample efficiency to MDPs that use hand-designed dense reward functions.
Related papers
- ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization [41.074747242532695]
Online Reward Selection and Policy Optimization (ORSO) is a novel approach that frames shaping reward selection as an online model selection problem.
ORSO employs principled exploration strategies to automatically identify promising shaping reward functions without human intervention.
We demonstrate ORSO's effectiveness across various continuous control tasks using the Isaac Gym simulator.
arXiv Detail & Related papers (2024-10-17T17:55:05Z) - 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) - Rewards Encoding Environment Dynamics Improves Preference-based
Reinforcement Learning [4.969254618158096]
We show that encoding environment dynamics in the reward function (REED) dramatically reduces the number of preference labels required in state-of-the-art preference-based RL frameworks.
For some domains, REED-based reward functions result in policies that outperform policies trained on the ground truth reward.
arXiv Detail & Related papers (2022-11-12T00:34:41Z) - Distributional Reward Estimation for Effective Multi-Agent Deep
Reinforcement Learning [19.788336796981685]
We propose a novel Distributional Reward Estimation framework for effective Multi-Agent Reinforcement Learning (DRE-MARL)
Our main idea is to design the multi-action-branch reward estimation and policy-weighted reward aggregation for stabilized training.
The superiority of the DRE-MARL is demonstrated using benchmark multi-agent scenarios, compared with the SOTA baselines in terms of both effectiveness and robustness.
arXiv Detail & Related papers (2022-10-14T08:31:45Z) - Generative Augmented Flow Networks [88.50647244459009]
We propose Generative Augmented Flow Networks (GAFlowNets) to incorporate intermediate rewards into GFlowNets.
GAFlowNets can leverage edge-based and state-based intrinsic rewards in a joint way to improve exploration.
arXiv Detail & Related papers (2022-10-07T03:33:56Z) - 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) - 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) - Efficient Exploration of Reward Functions in Inverse Reinforcement
Learning via Bayesian Optimization [43.51553742077343]
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.
arXiv Detail & Related papers (2020-11-17T10:17:45Z) - DORB: Dynamically Optimizing Multiple Rewards with Bandits [101.68525259222164]
Policy-based reinforcement learning has proven to be a promising approach for optimizing non-differentiable evaluation metrics for language generation tasks.
We use the Exp3 algorithm for bandits and formulate two approaches for bandit rewards: (1) Single Multi-reward Bandit (SM-Bandit); (2) Hierarchical Multi-reward Bandit (HM-Bandit)
We empirically show the effectiveness of our approaches via various automatic metrics and human evaluation on two important NLG tasks.
arXiv Detail & Related papers (2020-11-15T21:57:47Z) - Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping [71.214923471669]
Reward shaping is an effective technique for incorporating domain knowledge into reinforcement learning (RL)
In this paper, we consider the problem of adaptively utilizing a given shaping reward function.
Experiments in sparse-reward cartpole and MuJoCo environments show that our algorithms can fully exploit beneficial shaping rewards.
arXiv Detail & Related papers (2020-11-05T05:34:14Z)
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.