5 papers
General Flexible -divergence for Challenging Offline RL Datasets with Low Stochasticity and Diverse Behavior Policies
Jianxun Wang, Grant C. Forbes, Leonardo Villalobos-Arias +1
Offline RL algorithms aim to improve upon the behavior policy that produces the collected data while constraining the learned policy to be within the support of the dataset. Howeve…
Minding Motivation: The Effect of Intrinsic Motivation on Agent Behaviors
Leonardo Villalobos-Arias, Grant Forbes, Jianxun Wang +2
Games are challenging for Reinforcement Learning~(RL) agents due to their reward-sparsity, as rewards are only obtainable after long sequences of deliberate actions. Intrinsic Moti…
Action-Dependent Optimality-Preserving Reward Shaping
Grant C. Forbes, Jianxun Wang, Leonardo Villalobos-Arias +2
Recent RL research has utilized reward shaping--particularly complex shaping rewards such as intrinsic motivation (IM)--to encourage agent exploration in sparse-reward environments…
Autonomous Curriculum Design via Relative Entropy Based Task Modifications
Muhammed Yusuf Satici, Jianxun Wang, David L. Roberts
Curriculum learning is a training method in which an agent is first trained on a curriculum of relatively simple tasks related to a target task in an effort to shorten the time req…
Potential-Based Intrinsic Motivation: Preserving Optimality With Complex, Non-Markovian Shaping Rewards
Grant C. Forbes, Leonardo Villalobos-Arias, Jianxun Wang +2
Recently there has been a proliferation of intrinsic motivation (IM) reward-shaping methods to learn in complex and sparse-reward environments. These methods can often inadvertentl…