RL Weekly is a weekly newsletter highlighting important progress in reinforcement learning in research or industry.
RL Weekly 35: Escaping Local Optimas in Distance-based Rewards and Choosing the Best Teacher
In this issue, we look at an algorithm that use sibling trajectories to escape local optimas in distance-based shaped rewards, and an algorithm that dynamically chooses the best demonstrator teacher to train the student.
RL Weekly 34: Dexterous Manipulation of the Rubik's Cube and Human-Agent Collaboration in Overcooked
In this issue, we look at a robot hand manipulating and "solving" the Rubik's Cube. We also look at comparative performances of human-agnostic and human-aware RL agents in Overcooked when paired with human players.
RL Weekly 33: Action Grammar, the Squashing Exploration Problem, and Task-relevant GAIL
In this issue, we look at Action Grammar RL, a hierarchical RL framework that adds new macro-actions, improving performance of DDQN and SAC in Atari environments. We then look at a new algorithm that borrows just the benefits of SAC's bounded actions to TD3 to achieve better performance. Finally, we look at an improvement to GAIL on raw pixel observations by focusing on task-relevant details.
RL Weekly 32: New SotA Sample Efficiency on Atari and an Analysis of the Benefits of Hierarchical RL
In this issue, we look at LASER, DeepMind's improvement to V-trace that achieves state-of-the-art sample efficiency in Atari environments. We also look at Google AI and UC Berkeley's study on hierarchical RL, analyzing and isolating the reason behind the benefits of hierarchical RL.
RL Weekly 31: How Agents Play Hide and Seek, Attraction-Repulsion Actor Critic, and Efficient Learning from Demonstrations
In this issue, we look at OpenAI's work on multi-agent hide and seek and the behaviors that emerge. We also look at Mila's population-based exploration that exceeds the performance of various TD3 and SAC baselines. Finally, we look at DeepMind's R2D3, a new algorithm to learn from demonstrations.
RL Weekly 30: Learning State and Action Embeddings, a New Framework for RL in Games, and an Interactive Variant of Question Answering
In this issue, we look at a representation learning method to train state and action embeddings paired with TD3. We also look at a new framework with 20+ environments and algorithms from multiple fields of RL. Finally, we look at a new take on using RL for Question Answering (QA).
RL Weekly 29: The Behaviors and Superstitions of RL, and How Deep RL Compares with the Best Humans in Atari
In this issue, we look at reinforcement learning from a wider perspective. We look at new environments and experiments that are designed to test and challenge the agents' capabilities. We also compare existing RL agents against the playthroughs of best Atari human players.
RL Weekly 28: Free-Lunch Saliency and Hierarchical RL with Behavior Cloning
This week, we first look at Free-Lunch Saliency, a built-in interpretability module that does not deteriorate performance. Then, we look at HRL-BC, a combination of high-level RL policy with low-level skills trained through behavior cloning.
RL Weekly 27: Diverse Trajectory-conditioned Self Imitation Learning and Environment Probing Interaction Policies
This week, we look at a self imitation learning method that imitates diverse past experience for better exploration. We also summarize an environment probing policy that helps an agent adapt to different environments.
RL Weekly 26: Transfer RL with Credit Assignment and Convolutional Reservoir Computing for World Models
This week, we summarize a new transfer learning method using the Transformer reward model, and a world model controller that does not require training the feature extraction.
RL Weekly 25: Replacing Bias with Adaptive Methods, Batch Off-policy Learning, and Learning Shared Model for Multi-task RL
In this issue, we focus on replacing inductive bias with adaptive solutions (DeepMind), learning off-policy from expert experience (Google Brain), and learning a shared model for multitask RL (Stanford).
RL Weekly 24: Benchmarks for Model-based RL and Bonus-based Exploration Methods
This week, we summarize two benchmark papers. The first paper benchmarks 11 model-based RL algorithms in 18 continuous control environments, and the second paper benchmarks 4 bonus-based exploration methods in 9 Atari environments. Both papers agree that a standardized benchmark is needed for an objective analysis of new algorithms.
RL Weekly 23: Decentralized Hierarchical RL, Deep Conservative Policy Iteration, and Optimistic PPO
This week, we first introduce a ensemble of primitives without a high-level meta-policy that can make decentralized decisions. We then look at an deep learning extension of Conservative Policy Iteration that borrows the idea of DQN. Finally, we look at Optimistic PPO, an extension of PPO that encourages exploration through uncertainty bellman equation.
RL Weekly 22: Unsupervised Learning for Atari, Model-based Policy Optimization, and Adaptive-TD
This week, we first look at ST-DIM, an unsupervised state representation learning method from MILA and Microsoft Research. We also check UC Berkeley's new policy optimization method that uses model-based branch rollouts. Finally, we look at Adapative-TD, a new method of mixing MC and TD from DeepMind, Google Research and Universitat Pompeu Fabr.
RL Weekly 21: The interplay between Experience Replay and Model-based RL
This week, we introduce three papers on replay-based RL and model-based RL. The first paper introduces SoRB, a way to combine experience replay and planning. The second paper introduces a consistency loss to ensure that a model is consistent with the real environment. The final paper compares model-based agents with replay-based agents.
RL Weekly 20: Minecraft Competition, Off-policy Policy Evaluation via Classification, and Soft-attention Agent for Interpretability
This week, we introduce MineRL, a new RL competition using human priors to solve Minecraft. We also introduce OPE, a method of off-policy evaluation through classification, and a soft-attention agent for greater interpretability.
RL Weekly 19: Curious Object-Based Search Agent, Multiplicative Compositional Policies, and AutoRL
This week, we introduce combining unsupervised learning, exploration, and model-based RL; learning composable motor skills; and evolving rewards.
RL Weekly 18: Survey of Domain Randomization Techniques for Sim-to-Real Transfer, and Evaluating Deep RL with ToyBox
This week, we introduce a survey of Domain Randomization Techniques for Sim-to-Real Transfer and ToyBox, a suite of redesigned Atari Environments for experimental evaluation of deep RL.
RL Weekly 17: Information Asymmetry in KL-regularized Objective, Real-world Challenges to RL, and Fast and Slow RL
In this issue, we summarize the use of information asymmetry in KL regularized objective to regularize the policy, the challenges of deploying deep RL into real-world systems, and possible insights into psychology and neuroscience from deep RL.
RL Weekly 16: Why Performance Plateaus May Occur, and Compressing DQNs
In this issue, we introduce 'ray interference,' a possible cause of performance plateaus in deep reinforcement learning conjectured by Google DeepMind. We also introduce a network distillation method proposed by researchers at Carnegie Mellon University.
RL Weekly 15: Learning without Rewards: from Active Queries or Suboptimal Demonstrations
In this issue, we introduce VICE-RAQ by UC Berkeley and T-REX by UT Austin and Preferred Networks. VICE-RAQ trains a classifier to infer rewards from goal examples and active querying. T-REX learns reward functions from suboptimal demonstrations ranked by humans.
RL Weekly 14: OpenAI Five and Berkeley Blue
In this week's issue, we summarize the Dota 2 match between OpenAI Five and OG eSports and introduce Blue, a new low-cost robot developed by the Robot Learning Lab at UC Berkeley.
RL Weekly 13: Learning to Toss, Learning to Paint, and How to Explain RL
In this week's issue, we summarize results from Princeton, Google, Columbia, and MIT on training a robot arm to throw objects. We also look at a model-based DDPG developed by Peking University and Megvii that can reproduce pictures through paint strokes. Finally, we look at an empirical study by Oregon State University about explaining RL to layman.
RL Weekly 12: Atari Demos with Human Gaze Labels, New SOTA in Meta-RL, and a Hierarchical Take on Intrinsic Rewards
This week, we look at a new demo dataset of Atari games that include trajectories and human gaze. We also look at PEARL, a new meta-RL method that boasts sample efficiency and performance superior to previous state-of-the-art algorithms. Finally, we look at a novel method of incorporating intrinsic rewards.
RL Weekly 11: The Bitter Lesson by Richard Sutton, the Promise of Hierarchical RL, and Exploration with Human Feedback
In this issue, we first look at a diary entry by Richard S. Sutton (DeepMind, UAlberta) on Compute versus Clever. Then, we look at a post summarizing Hierarchical RL by Yannis Flet-Berliac (INRIA SequeL). Finally, we summarize a paper incorporating human feedback for exploration from Delft University of Technology.
RL Weekly 10: Learning from Playing, Understanding Multi-agent Intelligence, and Navigating in Google Street View
In this issue, we look at Google Brain's algorithm of learning by playing, DeepMind's thoughts on multi-agent intelligence, and DeepMind's new navigation environment using Google Street View data.
RL Weekly 9: Sample-efficient Near-SOTA Model-based RL, Neural MMO, and Bottlenecks in Deep Q-Learning
In this issue, we look at SimPLe, a model-based RL algorithm that achieves near-state-of-the-art results on Arcade Learning Environments (ALE). We also look at Neural MMO, a new multiagent environment by OpenAI, and an empirical analysis of possible sources of error in deep Q-learning by BAIR.
RL Weekly 8: World Discovery Models, MuJoCo Soccer Environment, and Deep Planning Network
In this issue, we introduce World Discovery Models and MuJoCo Soccer Environment from Google DeepMind, and PlaNet from Google.
RL Weekly 7: Obstacle Tower Challenge, Hanabi Learning Environment, and Spinning Up Workshop
This week, we introduce the Obstacle Tower Challenge, a new RL competition by Unity, Hanabi Learning Environment, a multi-agent environment by DeepMind, and Spinning Up Workshop, a workshop hosted by OpenAI.
RL Weekly 6: AlphaStar, Rectified Nash Response, and Causal Reasoning with Meta RL
This week, we look at AlphaStar, a Starcraft II AI, PSRO_rN, an evaluation algorithm encouraging diverse population of well-trained agents, and a novel Meta-RL approach for causal reasoning. All three results are from DeepMind.
RL Weekly 5: Robust Control of Legged Robots, Compiler Phase-Ordering, and Go Explore on Sonic the Hedgehog
This week, we look at impressive robust control of legged robots by ETH Zurich and Intel, compiler phase-ordering by UC Berkeley and MIT, and a partial implementation of Uber's Go Explore.
RL Weekly 4: Generating Problems with Solutions, Optical Flow with RL, and Model-free Planning
In this issue, we introduce new curriculum learning algorithm by Uber AI Labs, model-free planning algorithm by DeepMind, and optical-flow based control algorithm by Intel Labs and University of Freiburg.
RL Weekly 3: Learning to Drive through Dense Traffic, Learning to Walk, and Summarizing Progress in Sim-to-Real
In this issue, we introduce the DeepTraffic competition from Lex Fridman's MIT Deep Learning for Self-Driving Cars course. We also review a new paper on using SAC to control a four-legged robot, and introduce a website summarizing progress in sim-to-real algorithms.
RL Weekly 2: Tuning AlphaGo, Macro-strategy for MOBA, Sim-to-Real with conditional GANs
In this issue, we discuss hyperparameter tuning for AlphaGo from DeepMind, Hierarchical RL model for a MOBA game from Tencent, and GAN-based Sim-to-Real algorithm from X, Google Brain, and DeepMind.
RL Weekly 1: Soft Actor-Critic Code Release; Text-based RL Competition; Learning with Training Wheels
In this inaugural issue of the RL Weekly newsletter, we discuss Soft Actor-Critic (SAC) from BAIR, the new TextWorld competition by Microsoft Research, and AsDDPG from University of Oxford and Heriot-Watt University.