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Overfitting refers to a model being stuck in a local minimum while trying to minimise a loss function. In Reinforcement Learning the aim is to learn an optimal policy by maximising (not always but almost) a reward function rather than minimising a loss function, so you can't really talk of overfitting as in the supervised scenario, but you can definitely talk about sub-optimal policies.

If we think of a specific task like avoiding stationary objects, a simple sub-optimal policy would be to just stay still without moving at all, or moving in circles if the reward function was designed to penalise lack of movements.

The way to avoid an agent to learn sub-optimal policies is to find a good compromise between exploitation, i.e. the constant selection of the next action to take based on the maximum expected reward possible, and exploration, i.e. a random selection of the next action to take regardless of the rewards. Here's a link to an introduction to the topic: Exploration and Exploitation in Reinforcement Learning

It is worth mentioning that sometimes an agent can actually outsmart humans though, some examples are reported in this paper The Surprising Creativity of Digital Evolution. I particularly like the story of the insect agent trained to learn to walk while minimising the contact with the floor surface. The agent surprisingly managed to learn to walk without touching the ground at all. When the authors checked what was going on they discovered that the insect leaned to flip itself and then walk using its fake 'elbows' (fig7 in the linked paper). I add this story just to point out that most of the time the design of the reward function is itself even more important than exploration and exploitation tuning.

Overfitting refers to a model being stuck in a local minimum while trying to minimise a loss function. In Reinforcement Learning the aim is to learn an optimal policy by maximising or minimising a non-stationary objective-function which depends on the action policy, so overfitting is not exactly like in the supervised scenario, but you can definitely talk about sub-optimal policies.

If we think of a specific task like avoiding stationary objects, a simple sub-optimal policy would be to just stay still without moving at all, or moving in circles if the reward function was designed to penalise lack of movements.

The way to avoid an agent to learn sub-optimal policies is to find a good compromise between exploitation, i.e. the constant selection of the next action to take based on the maximum expected reward possible, and exploration, i.e. a random selection of the next action to take regardless of the rewards. Here's a link to an introduction to the topic: Exploration and Exploitation in Reinforcement Learning

It is worth mentioning that sometimes an agent can actually outsmart humans though, some examples are reported in this paper The Surprising Creativity of Digital Evolution. I particularly like the story of the insect agent trained to learn to walk while minimising the contact with the floor surface. The agent surprisingly managed to learn to walk without touching the ground at all. When the authors checked what was going on they discovered that the insect leaned to flip itself and then walk using its fake 'elbows' (fig7 in the linked paper). I add this story just to point out that most of the time the design of the reward function is itself even more important than exploration and exploitation tuning.

Overfitting refers to a model being stuck in a local minimum while trying to minimise a loss function. In Reinforcement Learning the aim is to learn an optimal policy by maximising (not always but almost) a reward function rather than minimising a loss function, so you can't really talk of overfitting as in the supervised scenario, but you can definitely talk about sub-optimal policies.

If we think of a specific task like avoiding stationary objects, a simple sub-optimal policy would be to just stay still without moving at all, or moving in circles if the reward function was designed to penalise lack of movements.

The way to avoid an agent to learn sub-optimal policies is to find a good compromise between exploitation, i.e. the constant selection of the next action to take based on the maximum expected reward possible, and exploration, i.e. a random selection of the next action to take regardless of the rewards. Here's a link to an introduction to the topic: Exploration and Exploitation in Reinforcement Learning

It is worth mentioning that sometimes an agent can actually outsmart humans though, some examples are reported in this paper The Surprising Creativity of Digital Evolution. I particularly like the story of the insect agent trained to learn to walk while minimising the contact with the floor surface. The agent surprisingly managed to learn to walk without touching the ground at all. When the authors checked what was going on they discovered that the insect leaned to flip itself and then walk using its fake 'elbows' (fig7 in the linked paper). I add this story just to point out that most of the time the design of the reward function is itself even more important than exploration and exploitation tuning.

Overfitting refers to a model being stuck in a local minimum while trying to minimise a loss function. In Reinforcement Learning the aim is to learn an optimal policy by maximising (not always but almost) a reward function rather than minimising a loss function, so you can't really talk of overfitting as in the supervised scenario, but you can definitely talk about sub-optimal policies.

If we think of a specific task like avoiding stationary objects, a simple sub-optimal policy would be to just stay still without moving at all, or moving in circles if the reward function was designed to penalise lack of movements.

The way to avoid an agent to learn sub-optimal policies is to find a good compromise between exploitation, i.e. the constant selection of the next action to take based on the maximum expected reward possible, and exploration, i.e. a random selection of the next action to take regardless of the rewards. Here's a link to an introduction to the topic: Exploration and Exploitation in Reinforcement Learning

It is worth mentioning that sometimes an agent can actually outsmart humans though, some examples are reported in this paper The Surprising Creativity of Digital Evolution. I particularly like the story of the insect agent trained to learn to walk while minimising the contact with the floor surface. The agent surprisingly managed to learn to walk without touching the ground at all. When the authors checked what was going on they discovered that the insect leaned to flip itself and then walk using its fake 'elbows' (fig7 in the linked paper). I add this story just to point out that most of the time the design of the reward function is itself even more important than exploration and exploitation tuning.

Overfitting refers to a model being stuck in a local minimum while trying to minimise a loss function. In Reinforcement Learning the aim is to learn an optimal policy by maximising (not always but almost) a reward function rather than minimising a loss function, so you can't really talk of overfitting as in the supervised scenario, but you can definitely talk about sub-optimal policies.

If we think of a specific task like avoiding stationary objects, a simple sub-optimal policy would be to just stay still without moving at all, or moving in circles if the reward function was designed to penalise lack of movements.

The way to avoid an agent to learn sub-optimal policies is to find a good compromise between eploitation, i.e. the constant selection of the next action to take based on the maximum expected reward possible, and exploration, i.e. a random selection of the next action to take regardless of the rewards. Here's a link to an introduction to the topic: Exploration and Exploitation in Reinforcement Learning

It is worth to mention that sometimes an agent can actually outsmart humans though, some examples are reported in this paper The Surprising Creativity of Digital Evolution. I particularly like the story of the insect agent trained to learn to walk while minimising the contact with the floor surface. The agent surprisingly managed to learn to walk without touching the ground at all. When the authors checked what was going on they discovered that the insect leaned to flip itself and then walk using its fake 'elbows' (fig7 in the linked paper). I add this story just to point out that most of the time the design of the reward function is itself even more important than exploration and exploitation tuning.

Overfitting refers to a model being stuck in a local minimum while trying to minimise a loss function. In Reinforcement Learning the aim is to learn an optimal policy by maximising (not always but almost) a reward function rather than minimising a loss function, so you can't really talk of overfitting as in the supervised scenario, but you can definitely talk about sub-optimal policies.

If we think of a specific task like avoiding stationary objects, a simple sub-optimal policy would be to just stay still without moving at all, or moving in circles if the reward function was designed to penalise lack of movements.

The way to avoid an agent to learn sub-optimal policies is to find a good compromise between exploitation, i.e. the constant selection of the next action to take based on the maximum expected reward possible, and exploration, i.e. a random selection of the next action to take regardless of the rewards. Here's a link to an introduction to the topic: Exploration and Exploitation in Reinforcement Learning

It is worth mentioning that sometimes an agent can actually outsmart humans though, some examples are reported in this paper The Surprising Creativity of Digital Evolution. I particularly like the story of the insect agent trained to learn to walk while minimising the contact with the floor surface. The agent surprisingly managed to learn to walk without touching the ground at all. When the authors checked what was going on they discovered that the insect leaned to flip itself and then walk using its fake 'elbows' (fig7 in the linked paper). I add this story just to point out that most of the time the design of the reward function is itself even more important than exploration and exploitation tuning.