![]() State, then we could easily construct a policy that maximizes our Us what our return would be, if we were to take an action in a given Our aim will be to train a policy that tries to maximize the discounted, Over stochastic transitions in the environment. Reinforcement learning literature, they would also contain expectations Our environment is deterministic, so all equations presented here areĪlso formulated deterministically for the sake of simplicity. If you are running this in Google Colab, run: Gym project and maintained by the same team since Gym v0.19. The action with the highest expected value isįirst, let’s import needed packages. The network is trained to predict the expected value for each action, Small fully-connected network with 2 outputs, one for each action. We take these 4 inputs without any scaling and pass them through a Values representing the environment state (position, velocity, etc.). The CartPole task is designed so that the inputs to the agent are 4 real This means better performing scenarios will runįor longer duration, accumulating larger return. Terminates if the pole falls over too far or the cart moves more than 2.4 Task, rewards are +1 for every incremental timestep and the environment Returns a reward that indicates the consequences of the action.
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