Memory-State Critic for Asymmetric Actor-Critic with Application to Vision-Based Pursuit-Evasion
The memory-state critic is a new asymmetric actor-critic method for partially observable Markov decision processes (POMDPs). It conditions on both the environment's state and the policy's internal memory, proving it yields unbiased policy gradients. This approach simplifies training by avoiding a second recurrent network needed by history-state critics while outperforming existing methods in vision-based pursuit-evasion tasks.