Memory-State Critic for Asymmetric Actor-Critic with Application to Vision-Based Pursuit-Evasion
In partially observable Markov decision processes, the optimal policy generally depends on the history of observations and past actions. Asymmetric actor-critic methods have become popular to learn such policies when additional information, such as the true state of the environment, is available during training. The critic, which is not needed at execution, is given access to the state. A critic conditioned on the state alone is generally ill-defined and yields biased policy gradients. Conditioning on the state and the history, the history-state critic restores both. In this paper, we show that conditioning the critic on the state and the policy's own memory, i.e., the internal representa...
| Authors | Arthur Louette, Alejandro Sánchez Roncero, Gaspard Lambrechts et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 October 2026 |
| Last updated at source | 12 October 2026 |
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