UvA

Stochastic elements in reinforcement learning (RL) have shown promise to improve exploration and handling of uncertainty, such as the utilization of stochastic weights in NoisyNets and stochastic policies in the maximum entropy RL frameworks. Yet effective and general approaches to include such elements in actor-critic models are still lacking. Inspired by the aforementioned techniques, we propose an effective way to inject randomness into actor-critic models to improve general exploratory behavior and reflect environment uncertainty. Specifically, randomness is added at the level of intermediate activations that feed into both policy and value functions to achieve better correlated and more complex perturbations. The proposed framework also features flexibility and simplicity, which allows straightforward adaptation to a variety of tasks. We test several actor-critic models enhanced with stochastic activations and demonstrate their effectiveness in a wide range of Atari 2600 games, a continuous control problem and a car racing task. Lastly, in a qualitative analysis, we present evidence of the proposed model adapting the noise in the policy and value functions to reflect uncertainty and ambiguity in the environment.