Generals replay player12/26/2023 Where \(a_i\) specifies the probability of taking action \(i\) while \(H(a)\) specifies the entropy of an action choice. We approximate both policy and value functions with neural networks which we denote with \(\pi_*GAE(a_i) + c * H(a)) The actor critic network can roughly be described in the diagram below We describe actor critic networks in more detail below. This combination allows us to get good results on many RL environments, especially when multiple networks are trained. This combination allows actor critic models to be applicable in both discrete and continuous action from policy estimation while value estimation allows us to reduce variance in training. Actor Critic NetworkĪ third approach towards deep reinforcement learning regards the actor critic model,used in this network, where we combine both policy estimation and value estimation. Traditionally, policy gradient algorithms have been used for discrete action spaces with stochastic policies through the Reinforce algorithm by randomly sampling each action, the deterministic policy gradient theorem allow policy gradients to be calculated even in deterministic policies, allowing estimation of policies on continuous actions in environments such as TORCS(simulated driving). Generally, we estimate a policy gradient for a particular action based off the discounted reward of the action, policy estimation can actually be used without computing any gradients at all by just choosing the best policy out a group of competing policies. In constract to value function estimation, where we estimate a policy based off values of state such as Q values, we derictly estimate policy based off discounted rewards of a particular action. Policy estimation involves directly estimating a policy for every state. A policy \(\pi\) maps a state \(s\) to a set of actions \(a\). Details can be found at the previous blog post Policy EstimationĪ second approach towards deep reinforcement learning regards policy estimation. Networks of this type include, deep Q networks, dueling Q network and many such variants. At test time, we then choose the action that has the highest reward with the current state. As a result, a modified version of value function estimation is used where we estimate the Q value or reward of a state and action pair. Since typically our algorithms are model-free, with just an value function estimate of a state, we are unfortunately unable to determine what action to take to actually reach a better state. The first, value function estimation, aims to estimate the approximate value of various states based off the expected rewards it will get in the future. Loosely, there are three different approaches towards deep reinforcement learning. Inspired by the AlphaGo paper, I decided to construct a series of networks towards playing the game of generals.io. Over the past 8 or so months I’ve had a lot of fun playing the game. What makes the game of generals environment interesting is that it is a imcomplete information game where each player is unaware of the actions of other players except when boundaries collide. Generals.io is a game where each player is spawned on an unknown location in the map and is tasked with expanding their land and capturing cities before eventually taking out enemy generals. Full code for A3C training and Generals.io Processing and corresponding replay.
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