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Found 8 papers, 5 papers with code
Evolving Curricula with Regret-Based Environment Design
Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex.
Replay-Guided Adversarial Environment Design
Furthermore, our theory suggests a highly counterintuitive improvement to PLR: by stopping the agent from updating its policy on uncurated levels (training on less data), we can improve the convergence to Nash equilibria.
A New Formalism, Method and Open Issues for Zero-Shot Coordination
We introduce an extension of the algorithm, other-play with tie-breaking, and prove that it is optimal in the LFC problem and an equilibrium in the LFC game.
Improving Social Welfare While Preserving Autonomy via a Pareto Mediator
Machine learning algorithms often make decisions on behalf of agents with varied and sometimes conflicting interests.
Accumulating Risk Capital Through Investing in Cooperation
Recent work on promoting cooperation in multi-agent learning has resulted in many methods which successfully promote cooperation at the cost of becoming more vulnerable to exploitation by malicious actors.
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
We call our technique Protagonist Antagonist Induced Regret Environment Design (PAIRED).
Quantifying Differences in Reward Functions
However, this method cannot distinguish between the learned reward function failing to reflect user preferences and the policy optimization process failing to optimize the learned reward.
Adversarial Policies: Attacking Deep Reinforcement Learning
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers.
