The value network was used at the leaf nodes to reduce the depth of the tree search. Now, instead of trying to give you yet another list of many things, I want you to analyze, I want you to make some of the conclusions. Roughly speaking, the value (function) based reinforcement learning is a large category of RL methods which take advantage of the Bellman equation and approximate the value function to find the optimal policy, such as SARSA and Q-Learning. >> >> It's gonna be fun! /Length 3865 By contrast, value based methods, such as Q-learning watkins1992q ; atarinature ; pdqn ; wangetal16 ; mnih2016asynchronous , can learn from any trajectory sampled from the same environment. So, you can of course, affect how the policy-based algorithms explore. /Contents 451 0 R /Annots [ 179 0 R 180 0 R 181 0 R 182 0 R 183 0 R 184 0 R 185 0 R 186 0 R 187 0 R 188 0 R 189 0 R 190 0 R 191 0 R 192 0 R 193 0 R 194 0 R 195 0 R 196 0 R 197 0 R 198 0 R 199 0 R ] Policy π determines which action will be choose by RL agent, and is usually state dependent [45]. Now that we defined the main elements of Reinforcement Learning, let’s move on to the three approaches to solve a Reinforcement Learning problem. Key Reinforcement Learning Terms for MDPs. Actor-critic combines the concept of Policy Gradient and Value-learning in solving an RL task. /Parent 1 0 R >> /Filter /FlateDecode /Annots [ 144 0 R 145 0 R 146 0 R 147 0 R 148 0 R 149 0 R 150 0 R 151 0 R 152 0 R 153 0 R 154 0 R 155 0 R 156 0 R 157 0 R 158 0 R ] We introduce a critic in evaluating a trajectory. << /Annots [ 91 0 R 92 0 R 93 0 R 94 0 R 95 0 R 96 0 R 97 0 R 98 0 R 99 0 R 100 0 R 101 0 R 102 0 R 103 0 R 104 0 R 105 0 R 106 0 R 107 0 R 108 0 R 109 0 R 110 0 R 111 0 R 112 0 R 113 0 R 114 0 R 115 0 R 116 0 R ] /Type /Page /Description-Abstract (We establish a new connection between value and policy based reinforcement learning \050RL\051 based on a relationship between softmax temporal value consistency and policy optimality under entropy regularization\056 Specifically\054 we show that softmax consistent action values correspond to optimal entropy regularized policy probabilities along any action sequence\054 regardless of provenance\056 From this observation\054 we develop a new RL algorithm\054 Path Consistency Learning \050PCL\051\054 that minimizes a notion of soft consistency error along multi\055step action sequences extracted from both on\055 and off\055policy traces\056 We examine the behavior of PCL in different scenarios and show that PCL can be interpreted as generalizing both actor\055critic and Q\055learning algorithms\056 We subsequently deepen the relationship by showing how a single model can be used to represent both a policy and the corresponding softmax state values\054 eliminating the need for a separate critic\056 The experimental evaluation demonstrates that PCL significantly outperforms strong actor\055critic and Q\055learning baselines across several benchmarks\056) /Contents 200 0 R We spent 3 previous modules working on the value-based methods: learning state values, action values and whatnot. << But Deep Q Learning is really great! >> OP seems to be aware of the difference between a value-based or policy-based model in RL. Such “off-policy” methods are able to exploit data from other sources, such as experts, making them inherently more sample efficient than on-policy methods guetal17 . Large applications of reinforcement learning (RL) require the use of generalizing func­ tion approximators such neural networks, decision-trees, or instance-based methods. endobj >> /Publisher (Curran Associates\054 Inc\056) Policy and Value Networks are used together in algorithms like Monte Carlo Tree Search to perform Reinforcement Learning. /MediaBox [ 0 0 612 792 ] applied the reinforcement learning approach in job shop scheduling problem (JSSP). /lastpage (2785) /MediaBox [ 0 0 612 792 ] Challenging (unlike many other courses on Coursera, it does not baby you and does not seem to be targeting as high a pass rate as possible), but very very rewarding.