Search Results for author:
Found 7 papers, 4 papers with code
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents
In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, but employ different strategies can be trained by adjusting the impact of individual objectives on reward function.
ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning
We propose ReCCoVER, an algorithm which detects causal confusion in agent's reasoning before deployment, by executing its policy in alternative environments where certain correlations between features do not hold.
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities
Additionally, we explore the differences between counterfactual explanations in supervised learning and RL and identify the main challenges that prevent the adoption of methods from supervised in reinforcement learning.
Causal Counterfactuals for Improving the Robustness of Reinforcement Learning
We apply CausalCF to complex robotic tasks and show that it improves the RL agent's robustness using CausalWorld.
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning
In this work, we propose RACCER, the first RL-specific approach to generating counterfactual explanations for the behavior of RL agents.
Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
Our approach allows the user to provide trajectory-level feedback on agent's behavior during training, which can be integrated as a reward shaping signal in the following training iteration.
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies
In this work, we propose ACTER (Actionable Counterfactual Sequences for Explaining Reinforcement Learning Outcomes), an algorithm for generating counterfactual sequences that provides actionable advice on how failure can be avoided.
