Rating-Based Reinforcement Learning
This paper develops a novel rating-based reinforcement learning (RbRL) approach that uses human ratings to obtain human guidance in reinforcement learning.
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- 1 The objective of this paper is to design a new ratingbased RL (RbRL) approach that infers reward functions via multi-class human ratings.
- 2 For example, IRL requires expert demonstrations and hence, cannot be directly applied to tasks that are difficult for humans to demonstrate.
- 3 In addition, due to their binary nature, standard preference queries do not indicate how much better or worse one sample is than another.
- 4 Inverse Reinforcement Learning (IRL) seeks to infer reward functions from demonstrations such that the learned reward functions generate behaviors that are similar to the demonstrations.
Introduction
With the development of deep neural network theory and improvements in computing hardware, deep reinforcement learning (RL) has become capable of handling complex tasks with large state and/or action spaces (e.g., Go and Atari games) and yielding human-level or better-than-humanlevel performance . Numerous approaches, such as DQN , DDPG , PPO , and SAC have been developed to address challenges such as stability, exploration, and convergence for various applications such.
Despite the important and fundamental advances behind these algorithms, one key obstacle for the wide application of deep RL is the required knowledge of a reward function, which is often unavailable in practical applications.
Another approach is to utilize qualitative human inputs indirectly to learn a reward function, such that humans guide reward function design without directly handcrafting the reward.
One limitation of ratings feedback is that the number of data samples in different rating classes can be very different, leading to imbalanced datasets.
Research Question
Although human experts could design reward functions in some domains, the cost is high because human experts need to understand the relationship between the mission objective and state-action values and may need to spend extensive time adjusting reward parameters and trade-offs not to encounter adverse behaviors such as reward hacking . The objective of this paper is to design a new ratingbased RL (RbRL) approach that infers reward functions via.
Methodology
The goal is to learn to perform a task by obtaining feedback from a teacher, in this case a synthetic human. We extend this method to select rating queries for the synthetic RbRL experiments, designing an ensemble-based approach as in Lee et al. to select trajectory segments for which to obtain synthetic ratings.
Study Design
In particular, our tests were approved by the UTSA IRB Office, including proper steps to ensure privacy and informed consent of all participants.
In particular, the goal is to learn to perform a given task by obtaining feedback from a teacher, in this case a human.
Thus, the performance of Cheetah and Swimmer can be evaluated via the hand-crafted environment rewards, while the Hopper task cannot be evaluated via its handcrafted environment reward.
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Results & Findings
For example, IRL requires expert demonstrations and hence, cannot be directly applied to tasks that are difficult for humans to demonstrate. First, each pairwise preference provides only a single bit of information, which can result in sample inefficiency.
- For example, IRL requires expert demonstrations and hence, cannot be directly applied to tasks that are difficult for humans to demonstrate.
- First, each pairwise preference provides only a single bit of information, which can result in sample inefficiency.
- In addition, due to their binary nature, standard preference queries do not indicate how much better or worse one sample is than another.
- Third, we conduct several experimental studies to quantify the impact of the number of rating classes on the performance of RbRL, and compare RbRL and PbRL.
- Our studies suggest that (1) too few or too many rating classes can be disadvantageous, (2) RbRL can outperform PbRL under both synthetic and real human.
For example, IRL requires expert demonstrations and hence, cannot be directly applied to tasks that are difficult for humans to demonstrate.
Future work includes developing mechanisms to quantify users’ consistency levels, the impact of user inconsistency, or solutions to user inconsistency.
Practical Applications
Thus, a PbRL algorithm may be more easily trapped in a local optimum, and cannot know to what extent its performance approaches the user’s goal. Finally, PbRL methods often require strict preferences, such that comparisons between similar-quality or incomparable trajectories cannot be used in reward learning. For example, when the number of rating classes is 5, the 5 possible human ratings could correspond to “very bad”, “bad”, “ok”, “good”, and.
It is worth mentioning that the statement “samples A and B are both rated as ‘good’ ” may provide more information than stating that “A and B are equally preferable”, which can be inferred by the former.
However, “A and B are equally preferable” may be important information for fine-tuning.
Hence, future work could integrate RbRL and PbRL to create a multi-phase learning strategy, where RbRL provides fast initial global learning while PbRL further refines performance via local queries based on sample pairs.
Thus, a PbRL algorithm may be more easily trapped in a local optimum, and cannot know to what extent its performance approaches the user’s goal. Finally, PbRL methods often require strict preferences, such that comparisons between similar-quality or incomparable trajectories.
Problem Formulation
Problem Formulation We consider a Markov decision process without reward (MDP\R) augmented with ratings, which is a tuple of the form (S, A, T, ρ, γ, n). Here, S is the set of states, A is the set of possible actions, T : S × A × S → [0, 1] is a state transition probability function specifying the probability p(s ′ | s, a) of reaching state s ′ ∈ S after taking action a in state s, ρ : S → [0, 1] specifies the initial state distribution.
Frequently Asked Questions
Although human experts could design reward functions in some domains, the cost is high because human experts need to understand the relationship between the mission objective and state-action values and may need to spend extensive time adjusting reward parameters and trade-offs not.
The goal is to learn to perform a task by obtaining feedback from a teacher, in this case a synthetic human. In particular, our tests were approved by the UTSA IRB Office, including proper steps to ensure privacy and informed consent of.
For example, IRL requires expert demonstrations and hence, cannot be directly applied to tasks that are difficult for humans to demonstrate. In addition, due to their binary nature, standard preference queries do not indicate how much better or worse one sample is.
For example, when the number of rating classes is 5, the 5 possible human ratings could correspond to “very bad”, “bad”, “ok”, “good”, and “very good”. These contributions are orthogonal to ours, as they could straightforwardly be applied within our proposed RbRL.
For example, IRL requires expert demonstrations and hence, cannot be directly applied to tasks that are difficult for humans to demonstrate. Future work includes developing mechanisms to quantify users’ consistency levels, the impact of user inconsistency, or solutions to user inconsistency.
This paper develops a novel rating-based reinforcement learning (RbRL) approach that uses human ratings to obtain human guidance in reinforcement learning.