Based on DeepMind publication, I've recreated the environment and I am trying to make the DQN find and converge to an optimal policy. The task of an agent is to learn how to sustainably collect apples (objects), with the regrowth of the apples depending on its spatial configuration (the more apples around, the higher the regrowth). So in short: the agent has to find how to collect as many apples as he can (for collecting an apple he gets a …
Following the tensorflow tutorial on deep reinforcement learning and DQN. Even after setting up the exact same libraries and running the same code, I am getting some error. from tf_agents.replay_buffers import reverb_utils .... rb_observer = reverb_utils.ReverbAddTrajectoryObserver( replay_buffer.py_client, table_name, sequence_length=2) # This line is throwing error This is the stacktrace TypeError Traceback (most recent call last) Input In [7], in <cell line: 23>() 15 reverb_server = reverb.Server([table]) 17 replay_buffer = reverb_replay_buffer.ReverbReplayBuffer( 18 agent.collect_data_spec, 19 table_name=table_name, 20 sequence_length=2, 21 local_server=reverb_server) ---> 23 …
So, I'm trying to implement alphazero's logic on the game of chess. What I understand so far of the algorithm is: Load 2 models, one of which is the best model you have so far. Both these models have a value network and a policy network and use MCTS to find the best move. Play n games between these 2 models and save the states, moves and who won each game Train the new model on a sample of the …
I am confused about the training stage of AlphaGo Zero using the data collected from the selfplay stage. According to an AlphaGo Zero Cheat Sheet I found, the training routine is: Loop from 1 to 1,000: Sample a mini-batch of 2048 episodes from the last 500,000 games Use this mini-batch as input for training (minimize their loss function) After this loop, compare the current network (after the training) with the old one (prior the training) However, after reading the article, …
Question on embedding similarity / nearest neighbor methods: In https://arxiv.org/abs/2112.04426 the DeepMind team writes: For a database of T elements, we can query the approximate nearest neighbors in O(log(T)) time. We use the SCaNN library [https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html] Could someone provide an intuitive explanation for this time complexity of ANN? Thanks! A very Happy New Year Earthling's!
I'm working on my chess bot, and I would like to implement simple artificial intelligence for it. I'm new in it, so I'm unsure how to do it specifically on chess. I heard about Q-learning, Supervised/Unsupervised learning, Genetic algorithm, etc., which probably is not for chess. I wondered how AlphaZero was created? Probably Genetic algorithm, but chess is the game where "if A then B" might not work. It means that Q-learning is also bad for it, and so on. …
I'm reading Grill's et al. paper regarding their self-supervised approach. I do not understand why the output of the target network is indicated as sg(z'ξ), rather then just (z'ξ), as would seem to be indicated from the loss equations? Is sg used simply for the sake of signifying that the results of this network do not impact it parameters (ξ)? Because that would seem redundant to how ξ is defined in the paper (as a weighted moving average of θ). …
Neuroscience is still trying to "find" how the mind (and language) somehow "works". Is there any theory linking a (low-dimensionality) embedding space (like word2Vec) to a mind (linguistic) model? Any Cognitive Linguistics theory?
In the MuZero paper pseudocode, they have the following line of code: hidden_state = tf.scale_gradient(hidden_state, 0.5) What does this do? Why is it there? I've searched for tf.scale_gradient and it doesn't exist in tensorflow. And, unlike scalar_loss, they don't seem to have defined it in their own code. For context, here's the entire function: def update_weights(optimizer: tf.train.Optimizer, network: Network, batch, weight_decay: float): loss = 0 for image, actions, targets in batch: # Initial step, from the real observation. value, reward, …
As far as I understood from the AlphaGo Zero system: During the self-play part, the MCTS algorithm stores a tuple ($s$, $\pi$, $z$) where $s$ is the state, $\pi$ is the distribution probability over the actions in the state and $z$ is an integer representing the winner of the game that state is in. The network will receive $s$ as input (a stack of matrices describing the state $s$) and will output two values: $p$ and $v$. $p$ is a …
I have been using epsilon greedy action selection strategy and recently have come across boltzmann(softmax) action selection strategy. One thing I am not clear about boltzmann exploration is the temperature variable. How should we define this variable. Is this a constant variable or should be decreased over the period of training. and how to decide on the absolute value of this parameter? Thanks
In one of the recent blog post by Deepmind, they have used game theory in Alpha Star algorithm. Deep Mind Alpha-Star: Mastering this problem requires breakthroughs in several AI research challenges including: Game theory: StarCraft is a game where, just like rock-paper-scissors, there is no single best strategy. As such, an AI training process needs to continually explore and expand the frontiers of strategic knowledge. Where the game theory is applied when it comes to reinforcement learning?
I am trying to implement a Deep Q Network model for Dynamic pricing in Logistics. I can define State Space (Origin, Destination, type of the shipment, customer, Type of the product, Commodity of the shipment, AVAILABILITY of capacity etc. Action Space (price itself, can range from 0 to inf) we need to determine the price itself. Reward Signal (Rewards can be based on a similar offer to other customers, seasonality, remaining capacity. I am planning to use Multi-Layer Perceptron for …
Going through the Deepmind jupyter notebook conditional neural processes, the plots at the bottom of the notebook show that the ground truth and the predicted distribution only overlap around the "context points". These context points are already in the training set. This comes as a surprise to me because I was expecting that if the model worked, then the ground truth curve would lie inside the predicted distribution at non-context points. So, doesn't this mean that the network failed to …