What are the main sources of randomness in reinforcement learning

Randomness of reward, given state and action.

Randomness of expected return given policy and MDP

Randomness of the action given state.

Randomness of the next state, given state and action

There is no randomness.

—————————————————————————————-

Mean reward that agent can get out from the environment, starting from state

s

and acting according to

\pi

Maximum reward that agent can get out from the environment, starting from state

s

and acting according to

\pi

Minimum reward that agent can get out from the environment, starting from state

s

and acting according to

\pi

Mean reward that agent can get out from the environment, starting from state

s

and acting according to optimal policy.

Maximum reward that agent can get out from the environment, starting from state

s

and acting according to optimal policy.

Minimum reward that agent can get out from the environment, starting from state

s

and acting according to optimal policy.

—————————————————————————————-

Mean reward, that agent can get out of the environment after making action

a

in state

s

and subsequently acting according to

\pi

Maximum reward, that agent can get out of the environment after making action

a

in state

s

and subsequently acting according to

\pi

Minimum reward, that agent can get out of the environment after making action

a

in state

s

and subsequently following current policy.

Mean reward, that agent can get out of the environment after making action

a

in state

s

and subsequently acting according to optimal policy.

Maximum reward, that agent can get out of the environment after making action

a

in state

s

and subsequently acting according to optimal policy.

Minimum reward, that agent can get out of the environment after making action

a

in state

s

and subsequently acting according to optimal policy.

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Only one.

Infinite.

One or more.

It depends on the particular MDP: there may be no optimal deterministic policies at all.

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An agent acts in the way that allows it to come to a state

s

that has highest

v_\pi(s)

for the current agent’s policy

\pi

In each state

s

the agent, following a policy

\pi

, makes an action

a

that maximizes

q_{\pi’}(s,a)

for some other policy

\pi’

In each state

s

the agent makes an action

a

that maximizes only the immediate reward

R (s, a, s^{'})

Provided each episode starts in the fixed state

s_0

, an agent plays the policy

π

that achieves the maximum possible

v_\pi(s_0)

across all possible policies.

In each state

s

an agent makes an action that maximises the value function

v_\pi(s’)

of the next state

s^{'}

for the current agent’s policy

\pi

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What are the main sources of randomness in reinforcement learning

Optimality in RL >>> What are the main sources of randomness in reinforcement learning >>> Practical Reinforcement Learning

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