Marvel strike force 0.3.1 best tank9/21/2023 It is in contrast with the deterministic policy which always mapped a given state to only one particular action. In stochastic policy, it returns a probability distribution of multiple actions in the action space for a given state. Deterministic Policy in Reinforcement Learning 2. So whenever the agent visits state M, J, C it will perform action Right, Up, and Right respectively. In the below gridworld example we have deterministic policy. When the agent reaches a given state, the deterministic policy tells it to perform a particular action always.Īt any given time t, for a given state s, the deterministic policy μ tells the agent to perform an action a. In a deterministic policy, there is only one particular action possible in a given state. Let us do a deep dive into each of these policies. In reinforcement learning, the policy can be categorized as follows – Types of Policy in Reinforcement Learning In our above animation, the policy shown is an optimal policy since it takes agents from the starting state M to the final goal state D.Ĭonsider the state J, if the agent performs action Left or Right it will die with negative reward, if it goes Down it is moving away from its goal, hence the optimal policy tells it to move Up. Optimal PolicyĪn optimal policy is a policy that yields good rewards for the agent and guides him to the goal. At this point in time, we can say that the agent has learned an optimal policy. Over several iterations, the agent starts learning the good policy that can produce positive rewards and can take him to its goal.
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