How do you handle partial observability and delayed rewards in actor-critic algorithms?

1 Partial observability

Partial observability means that the agent cannot access the full state of the environment, but only some observations that may be noisy or incomplete. This makes it harder for the agent to learn the optimal policy and value function, as it may not have enough information to make the best decisions. One way to handle partial observability is to use recurrent neural networks (RNNs) as the actor and critic, instead of feedforward networks. RNNs can store and process previous observations in their hidden states, and thus capture the temporal dependencies and dynamics of the environment.

2 Delayed rewards

Delayed rewards mean that the agent may not receive immediate feedback for its actions, but only after several steps or episodes. This makes it harder for the agent to assign credit or blame to its actions, and to update its policy and value function accordingly. One way to handle delayed rewards is to use n-step returns or generalized advantage estimation (GAE) as the target for the critic network. These methods reduce the variance and bias of the value function estimates, by using a combination of bootstrapping and Monte Carlo sampling. Another way to handle delayed rewards is to use entropy regularization or intrinsic motivation as additional rewards for the actor network. These methods encourage the agent to explore more and avoid getting stuck in local optima.

3 Advantages and disadvantages

Actor-critic algorithms have some advantages compared to other reinforcement learning methods, such as being able to learn both discrete and continuous actions, and both stochastic and deterministic policies. They can also balance the trade-off between exploration and exploitation by using the critic network to guide the actor network. However, these algorithms require more computational resources and training time than other methods, as they need to update two neural networks instead of one. Additionally, they may suffer from instability and divergence, as the actor network may overfit to the critic network or vice versa. Furthermore, they may be sensitive to hyperparameters and initialization, as they need to tune the learning rates, the discount factor, the entropy coefficient, and other factors.

4 Tips and tricks

To improve the performance and stability of your actor-critic algorithms, you may want to consider techniques such as batch normalization or layer normalization to normalize the inputs and outputs of the neural networks, and prevent gradient vanishing or exploding. You can also use gradient clipping or trust region methods to limit the magnitude of the gradient updates, as well as experience replay or parallel agents to collect and store more data. Additionally, target networks or polyak averaging can be used to update the critic network more slowly, and reduce the overestimation or oscillation of the value function.

5 Examples and resources

If you’re looking for examples and resources on how to use and implement actor-critic algorithms, you may want to explore the PyTorch and TensorFlow examples available on GitHub. Additionally, the Spinning Up in Deep RL and Reinforcement Learning: An Introduction websites provide comprehensive information on the subject. For a comprehensive overview, the Reinforcement Learning: An Introduction book is an excellent resource.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?