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- OpenAIDomain randomization and generative models for robotic graspingCompanies: OpenAIAbstract Deep learning-based robotic grasping has made significant progress thanks to algorithmic improvements and increased data availability. However, state-of-the-art models are often trained on as few as hundreds or thousands of unique object instances, and as a result generalization can be a challenge. In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp p
- OpenAICompetitive self-playCompanies: OpenAIWe’ve found that self-play allows simulated AIs to discover physical skills like tackling, ducking, faking, kicking, catching, and diving for the ball, without explicitly designing an environment with these skills in mind. Self-play ensures that the environment is always the right difficulty for an AI to improve. Taken alongside our Dota 2 self-play results, we have increasing confidence that self-play will be a core part of powerful AI systems in the future.
- OpenAIMeta-learning for wrestlingCompanies: OpenAIWe show that for the task of simulated robot wrestling, a meta-learning agent can learn to quickly defeat a stronger non-meta-learning agent, and also show that the meta-learning agent can adapt to physical malfunction.
- OpenAINonlinear computation in deep linear networksCompanies: OpenAI1 x = tf.placeholder(dtype=tf.float32, shape=[batch_size,784]) 2 y = tf.placeholder(dtype=tf.float32, shape=[batch_size,10]) 3 4 w1 = tf.Variable(np.random.normal(scale=np.sqrt(2./784),size=[784,512]).astype(np.float32)) 5 b1 = tf.Variable(np.zeros(512,dtype=np.float32)) 6 w2 = tf.Variable(np.random.normal(scale=np.sqrt(2./512),size=[512,512]).astype(np.float32)) 7 b2 = tf.Variable(np.zeros(512,dtype=np.float32)) 8 w
- OpenAILearning to model other mindsCompanies: OpenAIWe’re releasing an algorithm which accounts for the fact that other agents are learning too, and discovers self-interested yet collaborative strategies like tit-for-tat in the iterated prisoner’s dilemma. This algorithm, Learning with Opponent-Learning Awareness (LOLA), is a small step towards agents that model other minds.
- OpenAILearning with opponent-learning awarenessCompanies: OpenAIAbstract Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent reinforcement learning, but also can be extended to hierarchical RL, generative adversarial networks and decentralised optimisation. In all these settings the presence of multiple learning agents renders the training problem non-stationary and often leads to unstable training
- OpenAIOpenAI Baselines: ACKTR & A2CCompanies: OpenAIWe’re releasing two new OpenAI Baselines implementations: ACKTR and A2C. A2C is a synchronous, deterministic variant of Asynchronous Advantage Actor Critic (A3C) which we’ve found gives equal performance. ACKTR is a more sample-efficient reinforcement learning algorithm than TRPO and A2C, and requires only slightly more computation than A2C per update.
- OpenAIMore on Dota 2Companies: OpenAIOur Dota 2 result shows that self-play can catapult the performance of machine learning systems from far below human level to superhuman, given sufficient compute. In the span of a month, our system went from barely matching a high-ranked player to beating the top pros and has continued to improve since then. Supervised deep learning systems can only be as good as their training datasets, but in self-play systems, the available data improves automatically as the agent gets better.
- OpenAIDota 2Companies: OpenAIWe’ve created a bot which beats the world’s top professionals at 1v1 matches of Dota 2 under standard tournament rules. The bot learned the game from scratch by self-play, and does not use imitation learning or tree search. This is a step towards building AI systems which accomplish well-defined goals in messy, complicated situations involving real humans.
- OpenAIGathering human feedbackCompanies: OpenAIRL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions. The underlying technique was developed as a step towards safe AI systems, but also applies to reinforcement learning problems with rewards that are hard to specify.
- OpenAIBetter exploration with parameter noiseCompanies: OpenAIWe’ve found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This exploration method is simple to implement and very rarely decreases performance, so it’s worth trying on any problem.
- OpenAIProximal Policy OptimizationCompanies: OpenAIWe’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune. PPO has become the default reinforcement learning algorithm at OpenAI because of its ease of use and good performance.