There are many continuous action space algorithms in deep reinforcement learning topology. Some of them, which we covered earlier in Chapter 4, Policy Gradients, were mainly stochastic policy gradients and stochastic actor-critic algorithms. Stochastic policy gradients were associated with many problems such as difficulty in choosing step size owing to the non-stationary data due to continuous change in observation and reward distribution, where a bad step would adversely affect the learning of the policy network parameters. Therefore, there was a need for an approach that can restrict this policy search space and avoid bad steps while training the policy network parameters.
Here, we will try to cover some of the advanced continuous action space algorithms:
- Trust region policy optimization
- Deterministic policy gradients
