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How do actor-critic methods cope with partial observability and uncertainty in reinforcement learning?

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Reinforcement learning (RL) is a branch of machine learning that deals with learning from actions and rewards in an environment. However, many real-world problems are not fully observable, meaning that the agent cannot access all the relevant information at each time step. Moreover, the environment may be uncertain, meaning that the outcomes of actions may be stochastic or noisy. How can we design RL methods that can cope with partial observability and uncertainty, and still achieve optimal or near-optimal performance?

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Daniel Zalda?a

??Artificial Intelligence | Algorithms | Thought Leadership
Khushee Kapoor

UWaterloo | Master of Data Science and Artificial Intelligence (Co-op) | LinkedIn Top Voice for Data Science | Amongst…
Arta Asadi

Machine Learning Engineer@ Smart Land Solutions

1 Actor-critic methods

One popular class of RL methods that can handle partial observability and uncertainty are actor-critic methods. These methods combine two components: an actor and a critic. The actor is a policy that maps observations to actions, and the critic is a value function that estimates the expected return or advantage of each action. The actor and the critic are both updated using gradient-based algorithms, such as policy gradient or temporal difference learning, based on the feedback from the environment.

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Arta Asadi

Machine Learning Engineer@ Smart Land Solutions
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Actor-critic methods may considered as the most powerful in most RL problems. This is because of the intrinsic changes of the actor and critic architectures. Also the supervision and relationship between actor and critic itself is make the system more flexible.

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2 Partially observable Markov decision processes

A common framework for modeling partially observable and uncertain environments is the partially observable Markov decision process (POMDP). A POMDP is a generalization of a Markov decision process (MDP), where the agent does not observe the true state of the environment, but only a partial or noisy observation. A POMDP can be represented by a tuple of <S, A, T, R, O, Z>, where S is the set of states, A is the set of actions, T is the transition function, R is the reward function, O is the set of observations, and Z is the observation function.

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Daniel Zalda?a

??Artificial Intelligence | Algorithms | Thought Leadership
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Imagine trying to find your way in thick fog where only parts of the path are visible. In POMDPs, this represents partial observability. Actor-critic methods act as your guide, with the actor (your actions) navigating based on incomplete, foggy information, and the critic (evaluation) ensuring your choices are sensible given the circumstances. In a POMDP, the environment doesn’t reveal everything upfront. You have to work with hints. The actor acts on those hints, while the critic checks if those actions are leading toward success, adapting when necessary.

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Khushee Kapoor

UWaterloo | Master of Data Science and Artificial Intelligence (Co-op) | LinkedIn Top Voice for Data Science | Amongst the Top 0.5% Data Scientists on Kaggle
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The actor component of the method generates actions based on the current state, providing a policy that guides the agent's decision-making. However, due to the partial observability inherent in POMDPs, the actor needs to consider not only the observed state but also account for the uncertainty associated with unobservable information. The critic component evaluates the value of the current state. This helps the agent assess the desirability of its current situation and provides feedback on the quality of the actions taken. The critic's role becomes particularly important in dealing with uncertainty since it contributes to refining the value estimates by incorporating information about the observed and unobserved aspects of the environment.

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3 Recurrent neural networks

One challenge of applying actor-critic methods to POMDPs is that the actor and the critic need to process sequential observations and maintain a memory of the past. A common solution is to use recurrent neural networks (RNNs) as function approximators for the actor and the critic. RNNs are neural networks that have feedback loops or hidden states that can store information over time. RNNs can learn to encode relevant features from the observations and use them to generate actions and value estimates.

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Arta Asadi

Machine Learning Engineer@ Smart Land Solutions
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In some cases like financial environments, some of the data is in the temporal dependency due to the sequential nature of the problem. also partial observability may rise in this case. Using the neural networks which are capable of learning the temporal and sequential dependency like RNN and positional embedding in transformers or even using models like Mamba might help a lot.

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4 Bayesian methods

Another challenge of applying actor-critic methods to POMDPs is that the agent needs to cope with the uncertainty and ambiguity of the observations and the environment. A common solution is to use Bayesian methods, which are based on probabilistic reasoning and inference. Bayesian methods can model the agent's beliefs about the state of the environment, the dynamics of the environment, and the uncertainty of the observations. Bayesian methods can also update the agent's beliefs using Bayes' rule, based on the evidence from the observations and the actions.

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5 Model-based and model-free methods

A further challenge of applying actor-critic methods to POMDPs is that the agent needs to balance between exploration and exploitation, meaning that it needs to trade-off between gathering new information and using existing information. A common solution is to use either model-based or model-free methods, or a combination of both. Model-based methods use an explicit or implicit model of the environment to plan ahead and select actions that maximize expected rewards. Model-free methods use direct experience or samples from the environment to learn and improve the actor and the critic.

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6 Examples and applications

Actor-critic methods have been successfully applied to various partially observable and uncertain problems, such as robotics, natural language processing, computer vision, and games. For example, actor-critic methods have been used to control robots in dynamic and noisy environments, such as navigation, manipulation, and locomotion. Actor-critic methods have also been used to generate natural language responses or captions from images or videos, taking into account the context and the uncertainty of the input. Actor-critic methods have also been used to play complex games, such as poker, StarCraft, and Dota 2, where the agent needs to deal with hidden information, stochastic outcomes, and strategic opponents.

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Arta Asadi

Machine Learning Engineer@ Smart Land Solutions
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One of the most complex environments is the financial environment which are complex, sequential partial observable, uncertain and very volatile! Actor critic methods can be very useful in this environments.

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7 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?

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Reinforcement Learning

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How do actor-critic methods cope with partial observability and uncertainty in reinforcement learning?

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1 Actor-critic methods

2 Partially observable Markov decision processes

3 Recurrent neural networks

4 Bayesian methods

5 Model-based and model-free methods

6 Examples and applications

7 Here’s what else to consider

Reinforcement Learning

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