Collaborating Authors

A new Potential-Based Reward Shaping for Reinforcement Learning Agent Artificial Intelligence

Potential-based reward shaping (PBRS) is a particular category of machine learning methods which aims to improve the learning speed of a reinforcement learning agent by extracting and utilizing extra knowledge while performing a task. There are two steps in the process of transfer learning: extracting knowledge from previously learned tasks and transferring that knowledge to use it in a target task. The latter step is well discussed in the literature with various methods being proposed for it, while the former has been explored less. With this in mind, the type of knowledge that is transmitted is very important and can lead to considerable improvement. Among the literature of both the transfer learning and the potential-based reward shaping, a subject that has never been addressed is the knowledge gathered during the learning process itself. In this paper, we presented a novel potential-based reward shaping method that attempted to extract knowledge from the learning process. The proposed method extracts knowledge from episodes' cumulative rewards. The proposed method has been evaluated in the Arcade learning environment and the results indicate an improvement in the learning process in both the single-task and the multi-task reinforcement learner agents.

An introduction to Reinforcement Learning – freeCodeCamp


Reinforcement learning is an important type of Machine Learning where an agent learn how to behave in a environment by performing actions and seeing the results. In recent years, we've seen a lot of improvements in this fascinating area of research. Examples include DeepMind and the Deep Q learning architecture in 2014, beating the champion of the game of Go with AlphaGo in 2016, OpenAI and the PPO in 2017, amongst others. In this series of articles, we will focus on learning the different architectures used today to solve Reinforcement Learning problems. These will include Q -learning, Deep Q-learning, Policy Gradients, Actor Critic, and PPO.

Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning


This paper investigates the use of intrinsic reward to guide exploration in multi-agent reinforcement learning. We discuss the challenges in applying intrinsic reward to multiple collaborative agents and demonstrate how unreliable reward can prevent decentralized agents from learning the optimal policy. We address this problem with a novel framework, Independent Centrally-assisted Q-learning (ICQL), in which decentralized agents share control and an experience replay buffer with a centralized agent. Only the centralized agent is intrinsically rewarded, but the decentralized agents still benefit from improved exploration, without the distraction of unreliable incentives.

Key Concepts of Modern Reinforcement Learning


As the Agent interacts with the Environment, it learns a policy. A policy is a "learned strategy" that governs the agents' behaviour in selecting an action at a particular time t of the Environment. A policy can be seen as a mapping from states of an Environment to the actions taken in those states. The goal of the reinforcement Agent is to maximize its long-term rewards as it interacts with the Environment in the feedback configuration. The response the Agent gets from each state-action cycle (where an Agent selects an action from a set of actions at each state of the Environment) is called the reward function.

Scalable agent alignment via reward modeling: a research direction Artificial Intelligence

One obstacle to applying reinforcement learning algorithms to real-world problems is the lack of suitable reward functions. Designing such reward functions is difficult in part because the user only has an implicit understanding of the task objective. This gives rise to the agent alignment problem: how do we create agents that behave in accordance with the user's intentions? We outline a high-level research direction to solve the agent alignment problem centered around reward modeling: learning a reward function from interaction with the user and optimizing the learned reward function with reinforcement learning. We discuss the key challenges we expect to face when scaling reward modeling to complex and general domains, concrete approaches to mitigate these challenges, and ways to establish trust in the resulting agents.