Still, we all know you're kidding yourself and there are just too many memories attached to your old Atari games so you keep them in a little box in your linen closet. Would you consider selling those memories if they turn out to be lucrative? Imgur.com/Laseki As it turns out, if ...
powerful for generating trajectories through the state space. For cartpole, 16 walkers and 200 steps of the simulation will always give you a winning trajectory with absolutely no neural networks or function approximators. Tons of atari games have also been solved this way. See a chart of scores...
Coin-operated arcade amusement took a severe hit when Atari released the first home-gaming console, which was created by the founders of the famous arcade game Pong. Atari 2600 came equipped with two joysticks, paddle controllers, a wood-panel printed console, and game cartridges, including "Spa...
Further, although TVT improved performance on problems requiring exploration, for the game Montezuma’s Revenge, which requires the chance discovery of an elaborate action sequence to observe reward, the TVT mechanism was not triggered (Supplementary Fig. 21; see Supplementary Fig. 20 for an Atari ...
The small gaming system, which came packaged with the wildly popular Tetris, combined elements from Nintendo’s NES gaming console and the Game and Watch, the original 1980 handheld from the Japanese company. Although it was less advanced than competitors from Sega and Atari, the 30 hours of ...
We could find a lot of achievements brought by the DRL technology from (LeCun et al., 2015; Schmidhuber, 2015; Goodfellow et al., 2016). For example, (Mnih et al., 2015) utilized the DRL agent to learn the raw pixels of the Atari game and achieve human-level performance. (Silver ...
In fact, innovations across video games (e.g. Atari Pong), communication (e.g. ARPANET email and Motorola cell phone), storage (e.g. IBM floppy disk), content distribution (e.g. Philips VCR and Sony Walkman) and computing (e.g. Apple computer) during the 1970s are still powering ...
DQN [2] combines the deep neural network with the Q-learning algorithm, realizes the "end-to-end" control of agents, and achieves better results than human beings in many kinds of Atari games. Double-DQN [19] solves the problem of overestimation in DQN. Other value-based algorithms include...
The goal of RL is then to learn a policy, i.e., a function producing a sequence of actions yielding the highest cumulative reward. Despite its simplicity, RL achieved impressive results, such as beating Atari videogames from pixels [1,2], or world-class champions at Chess, Go and Shogi...
The goal of RL is then to learn a policy, i.e., a function producing a sequence of actions yielding the highest cumulative reward. Despite its simplicity, RL achieved impressive results, such as beating Atari videogames from pixels [1,2], or world-class champions at Chess, Go and Shogi...